EOverview
Earth Species Project is a nonprofit research lab and impact organization that uses artificial intelligence to study animal communication and work toward decoding non-human languages. It aims to help people understand how other species communicate and to broaden humanity’s perspective on nature. ESP’s approach centers on AI-driven analysis of animal vocalizations and behavior to interpret signals, with research and outreach designed to translate findings into practical insights about interspecies communication. What sets ESP apart is its nonprofit, mission-driven focus combined with a dedicated effort to translate animal communication into human understanding, rather than pursuing commercial product development. The goal is to enable humans to learn from other species and to shift how we relate to the natural world by expanding the boundaries of communication across species.
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About Earth Species Project
Industries
Company Size
11-50
Company Stage
Grant
Total Funding
$1.2M
Headquarters
Berkeley, CA
Founded
2018
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What believers are saying
- October 2026 ROOTS lifted unseen-species matching accuracy from 27% to 65%.
- Earth Species Project reported fourteen papers and new partnerships in its June 2026 annual report.
- Steven VanRoekel became CEO in March 2026, strengthening fundraising and execution discipline.
What critics are saying
- September 2026 ethics criticism flagged AI calls enabling poachers and wildlife manipulation.
- Licensing limits in alp-data restrict some datasets, slowing commercial and academic reuse.
- If regulators classify animal-synthesis tools as dual-use, Earth Species Project loses deployment trust.
What makes Earth Species Project unique
- Earth Species Project open-sourced BEANS-Next and ROOTS on October 21, 2026.
- NatureLM-audio targets bioacoustic tasks, not generic speech or music recognition.
- alp-data unifies 35-plus bioacoustic datasets into one reusable research layer.
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Broadening audio-language capabilities for bioacoustics: introducing BEANS-Next and ROOTS. October 21, 2026 Key takeaways: * Existing bioacoustic models are largely trained and evaluated on a narrow range of tasks, particularly species and call-type recognition, leaving many capabilities relevant to animal behavior and communication underexplored. * Earth Species Project introduce BEANS-Next, a benchmark grounded in a taxonomy of bioacoustics tasks spanning acoustic perception, biological category recognition, scene understanding, and in-context learning. * Earth Species Project also introduce ROOTS, a training resource containing 44 million audio-language pairs from 6.5 million distinct audio clips, built using existing bioacoustic data, metadata, language-model-generated text, and synthetic audio. * Fine-tuning NatureLM-audio with ROOTS substantially improved performance across all four tiers, including acoustic understanding, scene interpretation, and recognizing unfamiliar sounds from audio examples. * Earth Species Project has open-sourced BEANS-Next and ROOTS to support more versatile bioacoustic models. This research also informs the upcoming release of NatureLM-audio v2.0. The gaps in bioacoustic model training and evaluation. Its earlier work on NatureLM-audio demonstrated that a single audio-language model could perform multiple bioacoustic tasks, including species identification, call-type classification, and life-stage prediction. It also demonstrated promising zero-shot generalization to species unseen during training. However, bioacoustic training datasets and benchmarks still focus heavily on biological category recognition, particularly species and call-type classification. While these capabilities are valuable, they represent only a fraction of the questions researchers might want to ask of a recording. A further limitation lies in the training data itself. Bioacoustic archives contain vast collections of recordings, but many are only loosely labeled or accompanied by information stored in field notes and other unstructured formats. Turning these recordings and annotations into training examples for more complex bioacoustic tasks requires substantial processing. As a result, researchers lack both the benchmarks needed to evaluate these broader capabilities and the training resources to develop them. To address these limitations, Earth Species Project developed BEANS-Next, a new benchmark, and ROOTS, a large-scale audio-language training dataset. BEANS-Next: A benchmark covering four broader areas of bioacoustic capability. BEANS-Next is a new benchmark designed to evaluate a broader range of audio-language abilities relevant to bioacoustic research. It builds on previous benchmarking efforts and organizes bioacoustic tasks into four tiers: acoustic perception, biological category recognition, scene understanding, and in-context learning. For researchers working with recordings containing multiple species, overlapping vocalizations, or unfamiliar call types, identifying a species is often only one part of the analysis. Understanding when sounds occur, how they relate to one another, and how their acoustic properties differ can be equally important. Its evaluation of existing bioacoustic models on BEANS-Next revealed that they performed competitively on familiar recognition tasks but struggled with many of the broader capabilities. Addressing these limitations requires training data covering a wider range of bioacoustic tasks. These findings helped guide the development of ROOTS. ROOTS: building the training data these capabilities need. To address the training bottleneck, Earth Species Project developed ROOTS, a large-scale training dataset containing 44 million audio-language pairs from 6.5 million distinct audio clips. ROOTS makes more extensive use of existing bioacoustic recordings and annotations, while also introducing new ways to generate training examples across all four tiers of BEANS-Next. Earth Species Project used four complementary approaches to construct the dataset: * Curated bioacoustic archives: Earth Species Project brought together recordings and annotations from existing bioacoustic collections, converting information from different sources into a consistent format for model training. * Underused and audio-derived metadata: Earth Species Project incorporated information about species, taxonomy, call types, life stages, and behavior, alongside acoustic measurements and descriptions derived from recordings. * LLM-based text synthesis: Earth Species Project used language models to turn field notes and other unstructured annotations into natural-language questions and answers to build complex scene understanding tasks, and to generate descriptions of vocalization characteristics from spectrograms. Generated outputs were filtered and checked for unsupported or inaccurate information. * Synthetic audio pipelines: Earth Species Project created synthetic recordings using existing animal sounds, with precise labels and timestamps for individual events. These provided training examples for tasks including sound detection, counting, temporal reasoning, and recognizing sounds from audio examples. These methods allowed Earth Species Project to use more of the information already available in bioacoustic archives while creating training examples for tasks that are difficult to annotate at scale. What Earth Species Project learned from fine-tuning NatureLM-audio. To test whether ROOTS could improve model performance, Earth Species Project used NatureLM-audio v1.1 as a starting point and fine-tuned a separate model with training data aligned to each of the four BEANS-Next tiers. This allowed Earth Species Project to examine how targeted training data contributed to different bioacoustic capabilities. The results showed improvements across all four tiers, including tasks where existing models had previously struggled. Tier 1: acoustic perception. Training with ROOTS improved the model's ability to describe and measure acoustic characteristics, including fundamental frequency and signal-to-noise ratio. For example, in a multiple-choice question task with vocalization descriptions sourced from field notes, accuracy increased from 28.7% to 97.9%. Improvements in fundamental frequency estimation also extended to species held out from training, suggesting that some of these acoustic capabilities can generalize beyond the species represented in the training data. Performance improved on tasks involving species presence, call types, and behavior prediction. For example, accuracy on a behavior prediction multiple-choice question task sourced from field notes increased from 33.7% to 87.1%, demonstrating the potential value of using richer biological metadata during training. However, generating open-ended biological descriptions remained more challenging than predicting predefined categories. Tier 3: scene understanding. The models also improved on tasks requiring an understanding of how vocalizations relate to one another within a recording. These included identifying which species vocalized first, which sounds had the highest or lowest frequency, and whether vocalizations overlapped. Synthetic audio training provided examples with known event timings and acoustic characteristics, helping models learn these relationships. However, fine-grained event counting remained difficult, and improvements were not consistent across every task. Tier 4: in-context learning. One particularly promising result was the model's improved ability to recognize unfamiliar species using audio examples provided at inference time. On the unseen-species matching task, where the model compares a recording of an unseen species against candidate species recordings, accuracy increased from 27% to 65%. This capability could be particularly useful for bioacoustic research, where labeled recordings are often scarce. Rather than training a new classifier for every species or call type, researchers could provide a model with audio examples and ask it to identify matching sounds in other recordings. "Rather than training a new classifier for every species or call type, researchers could provide a model with audio examples and ask it to identify matching sounds in other recordings." Expanding what bioacoustic models can do. BEANS-Next provides a framework for evaluating the capabilities that bioacoustic research needs, while ROOTS offers new training resources to help develop them. Both are now openly available to support further research and development across the bioacoustics community. This research has also helped inform the development of NatureLM-audio v2.0, the next generation of its large audio-language foundation model for bioacoustics. Building on the work presented here, Earth Species Project is continuing to develop models that can support a wider range of bioacoustic research tasks, giving scientists more flexible tools to investigate animal sounds and communication. Earth Species Project'll be sharing more about NatureLM-audio v2.0 and its new capabilities soon. Stay tuned!
Experts are sounding the alarm on AI animal translation - breakthrough tool or new form of exploitation? Les-Leigh A September 8, 2026 Long before Dr Dolittle made talking to the animals look like a charming eccentric pursuit, bioacousticians were steadily drowning in vast, noisy vocalisation datasets that no human ear could ever hope to sort manually. Now, large AI models are finding actual structure in all the acoustic chatter. In Spain, biologists Vittorio Baglione and Daniela Canestrari spent decades assembling roughly 150,000 carrion crow calls, and neural networks are finally helping them identify subtle distinctions humans missed, including a dedicated call used to rally nest defences. Equivalent work is underway across marine and terrestrial species, using the same neural architectures behind human language models to study bottlenose dolphin whistles, sperm whale codas, killer whales, zebra finches, elephants and mice too. Researchers are keen to clarify that this isn't a translation in any traditional human sense. It's pure pattern discovery: isolating recurring vocal units and sequences, then assessing whether they consistently correlate with specific contexts such as alarms, nest defence or contact calls. To accelerate this work, the Earth Species Project launched NatureLM-audio, an open audio-language model for non-human sounds. Meanwhile, the $10 million Coller Doolittle Challenge is offering a massive payout to anyone who can establish two-way communication without the animal realising it's talking to a human, a feat backers believe could be achievable by 2030. The manipulation problem. What troubles bioethicists most is the immediate prospect of systemic manipulation. Once an AI model can accurately replicate or synthesise animal calls, influencing wildlife behaviour without anything approximating consent becomes too easy. Poachers can easily use synthetic mating calls to lure targets, while land management can broadcast distress loops to control animal movement. At its most extreme, broadcasting artificial whale calls across entire ocean sectors would modernise historical acoustic driving techniques, coercing marine life onto predictable paths. A 2025 commentary in a Springer Nature journal argues that current research ethics largely ignore animal privacy, the interest an animal might have in controlling how it appears to others, even as researchers harvest vocalisations and movement data at an industrial level. Even benign intentions don't make precise audio playback ethical. Zoologist Katy Payne illustrated this by observing severe distress in a living elephant exposed to the recorded voice of a deceased herd member, proving that high-fidelity audio reproduction isn't inherently responsible. Baglione also acknowledges this tension in his own crow-call studies. The very tool built to map and understand a distress signal equips anyone with the means to create false comfort or distress. Rules built for an earlier era. Formal safeguards across the field are virtually non-existent. Most of what currently governs this work exists inside general animal-research ethics guidelines that lack tailored provisions for AI-scale acoustic analysis or synthesis. Existing institutional review boards evaluate field experiments through traditional welfare metrics like physical tagging impacts, relying on legacy standards that weren't constructed with machine learning capabilities in mind. Existing AI ethics guidelines haven't caught up either. Bioethicists warn that the current standards fail to address dual-use vulnerabilities, which leaves a thin line between beneficial conservation models and harmful exploitation tools. The field's current incentive structures only heighten this friction. Rewarding covert two-way communication, as the Doolittle Challenge does, pushes technical boundaries while bypassing consent concerns. Who benefits depends entirely on who holds the tools. The positive applications of this technology are certainly compelling. Finding hidden vocal signatures tied to stress, danger, social structure or physical pain could radically advance welfare assessments in agricultural, clinical and wild settings, giving conservation teams data where they once relied on educated guesses. Even so, these extraordinary possibilities don't erase or diminish the inherent risks existing alongside them. The reality is that this technology is dual-use in the truest sense. The outcome depends entirely on who holds the keys to the models and acoustic playback hardware. A conservation researcher and an illegal poacher could easily operate using the same algorithm. Whether ethics policies adapt quickly enough to address animal consent and acoustic influence before this software becomes ubiquitous is a question the scientific community has yet to resolve.
Introducing alp-data: A shared data layer for Animal Language Processing. July 22, 2026 Key takeaways. * alp-data is a pip-installable Python package that provides unified access to 35+ bioacoustic datasets through one consistent interface. * This removes the per-dataset engineering work that often stands between researchers and the large, diverse training corpora required to study animal communication at scale. * alp-data is the first piece of shared infrastructure for Animal Language Processing (ALP), an approach to studying animal communication that is AI-powered, data-driven, and species agnostic. * Earth Species Project is also introducing the ALP Data Explorer, a visual tool to help anyone explore a subset of the cross-taxa data contained in alp-data. * Important note: Earth Species Project compiled and unified these datasets. Earth Species Project did not create them. Each one remains governed by the license its original creators set. * alp-data is free and hosted by ESP. Turning fragmented data into shared infrastructure. The study of animal communication is moving toward large-scale, AI-driven inquiries across populations, species, and taxa. Earth Species Project introduced the term Animal Language Processing (ALP) as a name for this emerging field of study that aims to answer questions like: How do animal dialects emerge across populations or geographies? How does communication change across populations with unique dialects? That shift - towards a data-driven, AI-powered, and species-agnostic approach - requires far more data infrastructure than the field currently has. While there's been an explosion in bioacoustic and other ecological data, much of it remains out of reach for training and evaluating large AI models. Today, Earth Species Project is introducing alp-data, the data access layer Earth Species Project use within its own organization, and one of the first pieces of Animal Language Processing infrastructure. Figure 1: Taxonomic diversity represented within the alp-data collection. These sunburst charts illustrate the breadth of mammalian (left) and avian (right) taxa currently included, with each ring representing progressively finer taxonomic levels from class to order and family. As Earth Species Project trained and evaluated its models across increasingly diverse datasets, Earth Species Project found ourselves repeatedly solving the same data-access problems. Over time, those internal tools evolved into a unified access layer that became part of its own research workflow. Today, Earth Species Project has open-sourced it so that the wider field can work from the same foundation. The Animal Language Processing bottleneck. The abundance of bioacoustic data is, by now, well established. Passive acoustic monitoring, animal-borne tags, and sustained recording efforts have produced data across species and environments at a scale that would have been difficult to imagine a decade ago. Now, the challenge is in turning that data abundance into models capable of meaningful generalization. Models generalize when they can perform well on data they have never seen before. In animal communication research, that means transferring what they have learned across species, taxa, and recording conditions. Achieving that kind of generalization requires training on diverse data at breadth and scale. "The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin." - Rich Sutton, The Bitter Lesson Its own work on bioacoustic encoding pointed in this direction: models like NatureLM-audio that are exposed to a wide diversity of training data, including general audio alongside bioacoustic recordings, generalize more reliably across tasks and species than smaller, domain-specific models. Yet building models that generalize across species requires substantial data engineering before any machine learning begins. Imagine you've identified five datasets that could help answer your research question. The problem? They are hosted on multiple platforms. BirdSet uses one schema, Xeno-Canto another, Watkins yet another. Each comes with its own sampling rates, file structures, metadata conventions, and licensing terms. Before you've trained a single model or run a single experiment, you find yourself spending days or even weeks making these datasets work together. Combining even a handful into a single training set requires writing and maintaining a bespoke data loader, alongside paying for storage, testing for consistency, tracking lineage and provenance, and allowing dataset schemas to evolve while maintaining backwards compatibility across versions. Every additional taxon (let alone other modalities like video or behavioral notes) enlarges the surface area of formats to be handled, so that the corpora most valuable for cross-taxa work - the broadest and most diverse - are also the most difficult to construct. What alp-data is. alp-data is a standardized point of access to bioacoustic datasets. It is a Python package installed with a single command (pip install alp-data) that provides access to 35+ bioacoustic datasets through one interface. Whatever a dataset's original format or location, it is loaded, streamed, and combined in the same manner as every other dataset in the collection. Each dataset behaves as a standard Python object that can be iterated over, indexed into, or streamed sample by sample when it exceeds available memory. Every sample returns its audio and sample rate in a consistent form, with dataset-specific labels and annotations carried alongside. The operations that normally precede training, including sampling-rate harmonization, label derivation, and split-aware loading, are handled within the interface itself. Datasets can be filtered, reshaped, and concatenated or chained into a single combined dataset for training or evaluation, which means that assembling a broad multi-taxon corpus becomes a matter of composition rather than of writing and maintaining a new pipeline for each source. Datasets can be declared, combined, or transformed all from a YAML config file allowing for reproducibility in training and evaluation runs. Earth Species Project use alp-data extensively to drive several recent publications and in its open source packages like AVEX. "Organizing and preprocessing data used to be one of the most time consuming parts of my research. With alp-data, that's largely automated, freeing up more time for the part I love most, digging into the results." - Sara Keen, ESP Senior Research Scientist ALP Data Explorer. Alongside alp-data, Earth Species Project is also introducing the ALP Data Explorer, giving anyone a simple way to explore a curated selection of the bioacoustic datasets available, directly in your browser. You can browse recordings across species and taxa, explore where they were collected, and listen to audio samples without writing a line of Python. While the Explorer showcases only a subset of the available collections, it provides an approachable introduction to the datasets behind alp-data. As alp-data continues to evolve, Earth Species Project hope tools like the explorer can make it easier to discover datasets, compare collections, and navigate the growing landscape of animal communication data. Data ownership and licensing. alp-data provides access to datasets, but Earth Species Project doesn't own any of the datasets, nor did Earth Species Project create them. Earth Species Project compiled and unified access to work that belongs to the researchers and institutions who recorded, annotated, and published them. The majority of these datasets are openly licensed, though terms vary, and each retains the license its creators assigned. License and source metadata travel with the data and remain available per dataset, because provenance is as much a part of responsible infrastructure as access is. You can find more about the datasets included in alp-data here. Data contributions. Researchers with bioacoustic datasets interested in making them more accessible, are warmly invited to get in touch. Earth Species Project is reviewing data contributions on a case-by-case basis as the collection grows and Earth Species Project would love to hear from you! Earth Species Project offered Animal Language Processing as a provisional definition, meant to be sharpened and refined through use. alp-data is offered in the same spirit, as a first piece of shared infrastructure that the field can build on together. alp-data limitations. Current limitations of alp-data include: * alp-data covers bioacoustics alone. * Its coverage leans toward birds, a reflection of where openly licensed data is most plentiful. * Licensing and credit constrain access to a subset of datasets, which appear named and credited even in cases where the underlying audio cannot be retrieved through the interface. * The package assumes a working familiarity with Python and machine learning, and so it is not yet suited to researchers without a computational background. Getting started. The package can be installed directly: pip install alp-data Earth Species Project has created seven tutorial notebooks to help you get started. They cover the library's core features, such as loading your first dataset, working with PyTorch DataLoaders, and combining datasets for training and evaluation. Documentation, tutorials, and the full dataset listing are available on the project page, and the source is on GitHub. To submit feedback or join the discussion on alp-data, visit its forum on Discourse. Coming soon. * More datasets, including Passive Acoustic Monitoring (example: direct-from-bucket streaming). * Enhanced performance and scalability through more advanced backend technologies and data management tools. * Strengthened dataset organization to improve reproducibility and benchmarking across studies. * Expand beyond bioacoustics to support other modalities relevant to animal communication, including video, behavioral data, movement data, and field observations. * Develop the platform in collaboration with the broader research community, guided by real-world research needs and feedback.
Help Raincoast give killer whales a voice. Raincoast has a little over three months to raise $77,500 for its marine expedition to take place! By Lauren Duboisset-Broust 2026 March 18 3 min. read Last December, Raincoast launched the first phase of its killer whale communication campaign. Thanks to your generosity, Raincoast attained its goal in only a couple of weeks. Last summer, researchers from Raincoast Conservation Foundation, Earth Species Project, and Icelandic Orca Project completed a highly successful pilot season in British Columbia. Raincoast tested and refined its multimodal workflow, demonstrated the feasibility of collecting synchronized datasets under challenging field conditions, and validated its analytical methods. This pilot season laid the foundation for a scalable and long-term research program - one that will ultimately allow Raincoast to protect killer whale societies from the impacts of ocean noise. Its team of scientists is preparing for an ambitious 2026 field season aboard Raincoast's Sailing Vessel Achiever. These extended periods of time at sea are necessary to collect the data that will bring Raincoast closer to understanding - and protecting - killer whales. Raincoast is thrilled to announce that one of its partners has committed to providing half of the funds required for this marine expedition to take place. That means Raincoast has a little over 3 months to raise the remaining $77,500. You can track its progress and learn more about this initiative. Your contribution directly fuels science, helping Raincoast get the vessel, its researchers, and its equipment into the field so Raincoast can continue advancing this critical research. About this campaign. Noise pollution is profoundly disrupting the whales' ability to communicate. It is a threat that is invisible to Raincoast, but devastating to them. To killer whales, sound is survival. They rely on calls to feed, to coordinate, and to maintain relationships. But with the ocean getting louder from vessel traffic, their vocalizations are being drowned out. Raincoast Conservation Foundation has joined forces with Earth Species Project, a non-profit research lab using machine learning to advance its understanding of animal communication. At the heart of this project is a deceptively simple idea: to watch and listen at the same time. By synchronizing drone footage, underwater acoustic recordings, and time-stamped behavioral observations to the nearest second, the team aims to uncover how killer whales use sound to coordinate movements, share prey, and maintain social bonds - to ultimately understand and mitigate how noise pollution interferes with these processes.
Tech moves: ex-microsoft leader takes nonprofit CEO role; Google vet joins LinkedIn; Amazon leaders depart. by Lisa Stiffler on Mar 17, 2026 at 11:48 am - Steven VanRoekel, a longtime former Microsoft leader and U.S. chief information officer under President Obama, is now CEO of Earth Species Project (ESP). The non-profit research lab is using artificial intelligence to better understand animal communication in creatures from carrion crows to beluga whales. VanRoekel, who is based in Bend, Ore., said his career has focused on driving impact at scale, and that ESP is poised for big breakthroughs. AI can "unlock the mysteries of our planet, especially around animal communication," he said in an ESP blog. "Once we begin unlocking that mystery, we could see shifts on the scale of Copernican or Galilean moments in history: new science, new understanding, and perhaps most importantly, new relationships with our planet." - Krzysztof Duleba joined LinkedIn's Bellevue, Wash., office as a distinguished engineer in its infrastructure program. Duleba has spent his career at Google, working there for 18 years in roles across search, ads, maps, AI and cloud. In separate posts on LinkedIn, Duleba shared his career journey. "Eighteen years ago, a kid from rural Poland walked into Google with no idea what he was getting into. He walked out a very different engineer, a father of three, and - he hopes - a better person," Duleba wrote in announcing his Google departure. And regarding his new role: "LinkedIn is in the middle of a major infrastructure transformation, and the timing matters. I consider getting reliability economics right during this window, before agentic development fully hits, the difference between drowning in the AI wave and catching it." - London-based Dennis Stansbury is resigning from Amazon after more than 18 years. He has held a variety of leadership roles in European offices, most recently serving as a principal product manager for Prime Video and Amazon MGM Studios in the United Kingdom. "I started in Seattle in March 2008, shortly after Kindle launched but before Prime Video or Alexa were likely even ideas," Stansbury said on LinkedIn, adding that he's going "to take some time off and put more thought into what's next." - After nearly 14 years at Amazon, Miranda Chen is leaving her role as a director and technical advisor for leaders in worldwide corporate and business development. Chen, who is based in the San Francisco Bay Area, did not indicate her next move. "I first started working for Amazon at A9, a Bay Area subsidiary, where we could review the key metrics for our entire offsite advertising business in a single weekly meeting," she said on LinkedIn. "Now we have Amazon offices worldwide and Amazon Ads is a meaningfully large business." - Scott Lawson, Amazon director of Global Real Estate and Facilities (GREF) design and construction, is leaving his role. Seattle-based Lawson has been with Amazon for nearly nine years. He was previously with Clark Construction Group working on developments nationwide. Lawson hinted on LinkedIn that information on his "next chapter" would be coming soon. - Danielle Decatur is vice president of community engagement and communications for Cloverleaf Infrastructure, a startup based in Seattle and Houston that's coordinating between landowners and power providers to offer ready-to-build sites tailored for data centers. "I'll be dedicated to enabling data center infrastructure that works for and directly benefits communities," Decatur said on LinkedIn. The sector is facing pushback over concerns about energy prices and environmental impacts of the facilities. Decatur was previously at Microsoft for more than 14 years, working most recently as director of energy and sustainability. Cloverleaf co-founder Brian Janous is Microsoft's former vice president of energy. Earlier in her career, Decatur served with the U.S. Air Force and with FEMA. - Augmodo named Bradford Snow as chief technology officer. The Seattle startup is developing wearable tech for retail store employees and Snow will focus on Augmodo's technical vision and innovation strategy. Snow joined the company from Axon, which sells taser devices and body cameras. His career also includes leadership roles at multiple tech giants where he worked on a variety of virtual reality technologies such as AR and VR devices at Meta; Amazon's Alexa AI and health and wellness wearable tech; and HoloLens initiatives at Microsoft. - Abhishek Mathur is now chief technology and product officer for ServiceTitan, a California software giant building an agentic operating system to serve trades such as plumbing, electrical and roofing by automating workflows and supporting technicians in the field. "This sector remains one of the largest untapped opportunities for technology to drive meaningful impact," Mathur said on LinkedIn. Mathur, who is based in the Seattle area, has held engineering leadership roles at Meta and was at Microsoft for more than 11 years. He was most recently at Figma as senior VP of engineering. - Anush Kumar is now founder and CEO of Intelligent Systems, a Bellevue, Wash.-based startup that aims to "transform operational workflows" with AI tools. "We're on a mission to help enterprises stop piloting and start producing," Kumar said in a LinkedIn post that includes links to five articles explaining the team's approach. Kumar was previously head of product for agentic automation at Atlassian. Other past roles include VP of technology at Expedia Group, senior VP of product at Zendesk, and director roles at Oracle and Avanade. His first tech role was lead product manager at Microsoft. - Chris Cappello joined Provn as vice president of marketing. Cappello has worked in multiple marketing roles for companies including WE Communications, Marina Maher Communications and M-Squared. He and Provn CEO Nikesh Parekh both worked earlier in their careers at HouseValues, which rebranded as Market Leader. Provn, a new Seattle startup, wants companies to scrap the traditional resume and replace it with portfolios of real work and challenge-based assessments. - Fred Hutch Cancer Center appointed two new leaders. Dr. Mazyar Shadman and Dr. Vyshak Venur were named as deputy chief medical officers, effective April 1. Shadman will serve as deputy CMO for classical hematology, hematologic malignancies, transplant and immunotherapy, while Venur will serve as deputy CMO for solid tumor and acute care services. And two Fred Hutch researchers received endowed chairs: Dr. Soheil Meshinchi, a global leader in treatments for acute myeloid leukemia, was awarded the Dylan Burke Endowed Chair in Immunotherapy; and Holly Harris received the inaugural Bus Family Endowed Chair in recognition for her work in prevention, early detection and precision oncology for uterine, ovarian and breast cancers. - Seattle's Marianne Bichsel, former VP of external affairs at Comcast, has launched Engaged Public Affairs, a PR and policy firm advising "leaders at the intersection of government, public trust, and corporate responsibility." Bichsel's co-founders are Julie Anderson, who has served in city and Washington state government, and Natasha Jones, a longtime leader in King County government. - Theodora, a Seattle-area wine recommendation app, appointed Lindsey Singhavi as its founding marketing lead. - In case you missed it, GeekWire took deeper dives into these recent notable tech moves (in no particular order, except maybe the first item):
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About Earth Species Project
Industries
Company Size
11-50
Company Stage
Grant
Total Funding
$1.2M
Headquarters
Berkeley, CA
Founded
2018
There are no jobs for Earth Species Project right now.
Find jobs on Simplify and start your career today
We update Earth Species Project's jobs every few hours, so check again soon!Browse all jobs