Full-Time
Software Engineer
Cloud Engineering
Confirmed live in the last 24 hours
Kumo
51-200 employees
Generates and deploys predictive models
Mid, Senior
Mountain View, CA, USA
This is a hybrid position, which implies that the candidate will need to work in-office some days.
You match the following Kumo's candidate preferences
Employers are more likely to interview you if you match these preferences:
- BS (preferred MS, PhD.) in Computer Science or a related field
- 3+ years of experience writing production code in C++, Python, Go, or similar languages.
- Experience with Infrastructure-as-Code development (e.g., Terraform, CloudFormation, Ansible, Chef, Bash scripting, etc.)
- Experience with B2B SaaS and architecting experience in building a large-scale distributed system at scale
- Experience with productionizing cloud applications, including Docker and Kubernetes
- Experience with CI/CD and advanced packaging, versioning, and deployment strategies
- Hands-on experience with Kubernetes (e.g., EKS, GKS, AKS, or OpenSource) on public clouds (AWS, GCP) at scale
- Build and extend components of the core Kumo Cloud Infrastructure and Kumo infrastructure
- Define a culture of engineering excellence and operational efficiency, especially as it relates to development and productization
- Build and automate CI-CD pipelines, release tooling to support continuous delivery, and true zero-downtime deployments across different cloud providers using the latest cloud-native technologies
- Work on advanced tools developed for the world’s leading cloud-native machine learning engine that uses graph deep learning technology
- Develop the infrastructure microservices for features such as usage tracking, diagnostics, monitoring, and alerting at the cloud scale
- Lead automation efforts to streamline global deployment effort
- Build the Kumo ML Ops platform, which will be able to data drift, track model versions, report on production model performance, alert the team of any anomalous model behavior, and run programmatic A/B tests on production models.
- Experience with popular MLOps tooling from cloud vendors like GCP (Vertex AI), AWS (SageMaker), or Azure Machine Learning, MLFlow, Kubeflow, etc.
- Experience with managing popular Data platforms such as AWS EMR, Snowflake, Databricks, etc.
- Experience with industry standard security practices, such as security testing, vulnerability assessments, ISO27001, GRC, and risk under compliance
- Extensive experience with Docker/Containers, Jenkins/Flux/Argo, and Terraform in a Linux environment
- Experience with monitoring tools such as Prometheus, Grafana, etc.
- Proficiency in developing customer-facing Web Front Ends or public APIs/SDKs for the application
Kumo.ai specializes in creating and implementing accurate predictive models for organizations that need reliable forecasts for critical operations. Their platform uses Graph Neural Networks to analyze raw relational data, which removes the need for manual data preparation and enhances prediction accuracy and efficiency. Unlike many competitors, Kumo.ai's platform streamlines the entire Machine Learning lifecycle, from data preparation to model deployment, while also optimizing costs by eliminating unnecessary infrastructure. The goal of Kumo.ai is to provide a fast return on investment for its clients, which range from small businesses to large enterprises, by offering flexible deployment options and ensuring high service availability.
Company Size
51-200
Company Stage
Series B
Total Funding
$35.5M
Headquarters
Mountain View, California
Founded
2021
Simplify's Take
What believers are saying
- Kumo's $18M Series B funding will expand its platform and market reach.
- Integration with Snowpark Container Services enhances deep learning capabilities within Snowflake Data Cloud.
- Kumo's platform supports both SaaS and Private Cloud models, offering client flexibility.
What critics are saying
- Increased competition from Databricks' Marketplace may divert potential customers.
- The rise of multimodal AI could overshadow Kumo's current offerings.
- Rapid AI advancements by tech giants may set new industry standards Kumo must meet.
What makes Kumo unique
- Kumo.AI uses Graph Neural Networks for predictive modeling, eliminating manual feature engineering.
- The platform offers a SQL-like Predictive Querying Language for rapid AI model creation.
- Kumo.AI integrates with Snowflake's Native App Framework, enhancing model performance and scalability.
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Benefits
Stock Options
Medical Insurance
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Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↓ -5%2 year growth
↑ 1%Data scientists can build highly accurate predictive AI models faster with Kumo AISAN FRANCISCO, June 4, 2024 /PRNewswire/ -- Today at Snowflake Data Cloud Summit 2024 , Kumo AI , a leader in predictive AI, announced it has launched Kumo as a Snowflake Native App in Snowflake Marketplace in private preview. Kumo helps data scientists build highly accurate machine learning (ML) models that better predict user and customer behavior by combining graph learning over enterprise data with generative AI models trained on public data. Kumo's Snowflake Native App is available through Snowflake Marketplace, can be paid for with Snowflake Capacity commitment for eligible customers, and is built using the Snowflake Native App Framework, enabling data scientists to develop predictive models faster than ever before."We understand how difficult building high-performing ML models can be. Our partnership with Snowflake simplifies the process and eliminates many of the typical hurdles that data scientists experience," said Vanja Josifovski, co-founder and CEO of Kumo AI. "By coupling Snowflake's well-known scalability, speed, and reliability with Kumo's specialized AI to identify and maximize the signal hidden in Snowflake data, we can produce highly accurate predictive ML models that are based on the entirety of your data including multiple relational tables, text, and images. Joint customers can unlock the full potential of their Snowflake data to fuel data-driven decisions and make granular predictions such as personalized recommendations."Businesses are trying to maximize revenue-impacting KPIs like conversion, cart size, and retention rates using insights uncovered from historic data, but struggle with efficient approaches and significant model improvements
In the cutthroat world of artificial intelligence, tech behemoths are betting big on a new frontier: multimodal AI. As the shine of text-based chatbots dims, companies are gambling that the future belongs to AI assistants capable of seeing, hearing and conversing with users more naturally and intuitively. The battle for AI dominance has taken on a new dimension, with “multimodal” emerging as the latest buzzword. “The usefulness of multimodal AI comes from its ability to simultaneously process and analyze diverse types of data, such as text, images, audio and video,” ComplyControl Chief AI Officer Mikhail Dunaev told PYMNTS
Credit: cono0430, ShutterstockAn open letter signed by a range of big and small tech companies has committed to responsible development of Artificial Intelligence (AI) at a time of increased public focus on the technology. Four of the the most high-profile signatories of the letter are Google, Microsoft, Meta and OpenAI, four of the most influential firms in global AI products – the latter is notable for being the creator of widely used AI platform ChatGPT. Due to continued development, AI has become increasingly utilised for business purposes, such as automation of general day-to-day tasks, risk management, anti-money laundering and fraud prevention
MOUNTAIN VIEW, Calif. , Nov. 16, 2023 /PRNewswire/ -- Kumo.AI , a Sequoia Capital-backed predictive AI company, announced the general availability of its Kumo.AI platform, enabling rapid creation and deployment of state-of-the-art AI models on private enterprise data. AI practitioners can now use Kumo.AI's intuitive SQL-like Predictive Querying Language to build multiple task-specific AI models in a single day. Founded in 2021 by Stanford University Professor (Jure Leskovec) and former Airbnb and LinkedIn executives (Vanja Josifovski, Hema Raghavan), Kumo.AI helps enterprises unlock customer-focused use cases, such as personalization, churn and LTV prediction, fraud detection, and forecasting.Kumo, cutting edge AI that can query the future (PRNewsfoto/Kumo.AI)While generative AI excels at natural language understanding and generation, it lacks the capability to perform more complex, enterprise-specific tasks, such as predicting customer behavior or detecting fraudulent transactions. Kumo.AI is the first platform that bridges this gap by applying deep representation learning, a technology behind the current AI revolution, to enterprise data in data warehouses
Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More. Kumo, a deep learning platform for relational data, announced today at Snowflake Summit 2023 its integration of deep learning capabilities directly into the Snowflake Data Cloud through Snowpark Container Services.Snowflake’s recently introduced Snowpark Container Services expands the functionality of Snowpark. This update allows organizations to run third-party software and full-stack applications within their Snowflake accounts.According to Snowflake, with this integration customers can maximize their data potential by using cutting-edge tools while maintaining data security and eliminating the need for data movement.Moreover, Snowpark Container Services includes GPU support, which gives data science and machine learning teams a way to accelerate development and bridge the gap between model deployment and consistent data security and governance throughout the AI/ML lifecycle.