Appen
Provides human-annotated data for AI training
Engineer Intern – Summer Internship - GenAI Research
About the job
- Current enrollment in or recent completion of a Master’s or PhD in Computer Science, AI, Machine Learning, Computer Engineering, or a closely related technical field.
About the company
Appen creates and curates large datasets used to train and improve artificial intelligence. It relies on a global, crowdsourced workforce to produce human-annotated data across text, images, audio, and video, which powers AI systems such as search engines and social feeds. Its products include data annotation services and platforms (like Figure Eight) and specialized data like mobile location data (Quadrant) to support ML pipelines. The company differentiates itself through scale, a broad range of data types, and a history of strategic acquisitions that expand its capabilities (e.g., Leapforce for search relevance, Figure Eight for annotation tooling, Quadrant for location data), enabling end-to-end data supply for enterprise AI. Its goal is to help organizations build reliable AI by providing high-quality labeled data and to capitalize on opportunities in generative and enterprise AI while returning to profitability under new leadership.
Company Size
10,001+
Company Stage
IPO
Headquarters
Sydney, Australia
Founded
1996
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Simplify's Take
What believers are saying
- H1 FY2026 revenue rose 17.5% to US$119.9 million, with EBITDA turning positive.
- Management reaffirmed FY2026 guidance of US$270 million to US$300 million and 5%-10% EBITDA margin.
- Appen held US$44.7 million cash and no debt at June 2026.
What critics are saying
- Appen Global revenue fell 26.9% in H1 FY2026, and its EBITDA stayed negative.
- Google ended Appen's contract in March 2024, permanently shrinking legacy search revenue.
- CrowdGen payment failures damaged contributor trust; recurring glitches can choke supply by 2027.
What makes Appen unique
- Appen China grew 80.4% to US$76.2 million in H1 FY2026.
- CrowdGen centralizes data sourcing, annotation, and contributor payments across Appen's global workforce.
- Appen still serves LLM, search, and moderation workflows for enterprise customers.
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Benefits
Remote Work Options
Professional Development Budget
Flexible Work Hours
Company News
Micro1 hits $500M run rate as AI training data gold rush accelerates. AI data startup Micro1 reaches $500M gross run rate, signaling explosive growth in training data market PUBLISHED: Fri, Aug 21, 2026, 12:49 AM UTC | UPDATED: Wed, Sep 2, 2026, 1:32 AM UTC The race to feed AI models just hit a new milestone. Micro1, a startup specializing in AI training data and data labeling services, has reached $500 million in gross run rate, according to TechCrunch. The achievement underscores how the AI boom isn't just enriching chip makers and model developers - it's creating massive opportunities for companies that provide the fuel these systems need to learn. As OpenAI, Google, and others race to build more capable models, the infrastructure powering that development is becoming a billion-dollar business in its own right. Micro1 just became the latest proof that in the AI gold rush, selling picks and shovels can be just as lucrative as mining for gold. The startup's climb to $500 million in gross run rate represents one of the fastest growth trajectories in the AI infrastructure space, fueled almost entirely by insatiable demand for training data. The numbers tell a story about where AI development bottlenecks really exist. While Nvidia grabs headlines with GPU shortages and OpenAI dominates conversations about model capabilities, companies like Micro1 are quietly solving a less glamorous but equally critical problem: how do you generate enough high-quality, human-labeled data to actually train these models? The answer involves armies of human annotators, sophisticated quality control systems, and increasingly complex workflows for reinforcement learning from human feedback - the technique that helped make ChatGPT feel conversational. Micro1's platform connects AI companies with skilled workers who label images, rate AI responses, and provide the feedback loops that turn raw compute into useful intelligence. What makes Micro1's growth particularly striking is the timing. The company is hitting this milestone just as the industry confronts a looming shortage of quality training data. According to research from Epoch AI, we could exhaust high-quality text data for training by 2026, forcing companies to get creative about synthetic data, better labeling, and more efficient use of human feedback. That scarcity is driving up the value of platforms that can reliably deliver quality annotations at scale. The competitive landscape reflects these high stakes. Scale AI, one of Micro1's chief rivals, hit a $7.3 billion valuation in 2021 and counts the Department of Defense among its clients. Companies like Labelbox, Sama, and Appen are all vying for contracts with the big AI labs. But Micro1's approach appears to be resonating - particularly its focus on reinforcement learning workflows that have become essential for training models like GPT-4 and Claude. The business model itself is revealing. Unlike pure software companies, data labeling operations blend technology platforms with managed workforces, creating gross run rates that look impressive but come with corresponding costs. Still, reaching $500 million signals that Micro1 has found a way to scale both sides of that equation effectively. For Google, Meta, Microsoft, and other companies racing to deploy AI across their product lines, reliable access to training data infrastructure has become non-negotiable. Internal teams can only scale so far. Outsourcing to specialists like Micro1 lets them move faster while maintaining the quality controls necessary for production AI systems. The growth also highlights how AI development is becoming more industrialized. Early models could be trained by small academic teams with modest budgets. Today's frontier models require coordinating thousands of labelers, managing complex feedback loops, and maintaining consistency across millions of data points. That operational complexity creates moats for companies that can execute it well. What's particularly interesting is how this market might evolve as AI capabilities advance. Some believe that as models get better, they'll need less human feedback - potentially reducing demand for labeling services. Others argue the opposite: that as AI tackles more complex tasks, the need for nuanced human judgment in training only increases. Micro1's bet is clearly on the latter scenario. The startup's trajectory also raises questions about market saturation. At $500 million in run rate, Micro1 is already processing enormous volumes of data. How much bigger can this market get? The answer may depend on how quickly AI adoption spreads beyond tech giants into healthcare, finance, manufacturing, and other industries that will need custom training data for domain-specific models. For now, the AI training data sector looks more like early innings than late. OpenAI is preparing even larger models, Google is embedding AI across its product suite, and enterprise adoption is just beginning. Each of those trends creates demand for more data labeling, more human feedback, and more infrastructure to manage it all. Micro1 appears to be capitalizing on that wave at exactly the right moment. Micro1's climb to $500 million in gross run rate isn't just a startup success story - it's a signal about where the AI industry's real infrastructure challenges lie. As models get more sophisticated and AI deployment accelerates across industries, the unglamorous work of labeling data and managing human feedback loops has become mission-critical. The companies that can deliver that infrastructure reliably and at scale are positioning themselves at the center of the AI economy. With training data scarcity looming and model complexity increasing, Micro1's growth trajectory suggests this market has plenty of room to run. The question now is whether the startup can maintain its momentum as competition intensifies and the technological landscape shifts beneath everyone's feet. More Topics:
Clover Corporation, an Australian producer of natural oils and encapsulated powders, has demonstrated strong earnings growth of 96.3% over the past year, despite a five-year decline. The company, with a market cap of A$166.07 million, is debt-free and maintains stable weekly volatility at 10%. Recent half-year results showed sales of A$44.09 million and net income of A$4.25 million, reflecting improved profit margins. The company's short-term assets of A$60.5 million comfortably exceed its combined short-term and long-term liabilities of A$13.7 million. Clover has announced a fully franked dividend and provided revenue guidance for fiscal 2026 between A$92 million and A$96 million, signalling confidence in its operational performance.
Appen reported mixed quarterly results, with overall revenue up 9% driven by China growth, but its Appen Global segment suffered a 37% revenue decline and EBITDA losses. The company maintained its full-year guidance of US$270 million to US$300 million revenue without upgrades. Investors responded negatively, pushing shares down 22.9%, interpreting the unchanged guidance as cautious despite China's improving performance. The update highlights the fragility of Appen's recovery, as persistent losses in Appen Global offset China's progress. The company reported a net loss of US$21.82 million for full-year 2025, underscoring ongoing profitability challenges. Analysts project the company could reach US$362 million in revenue by 2029, though pessimistic forecasts warn that automation and in-house tools could erode Appen's market share.
Appen, an AI lifecycle company specialising in data sourcing and annotation, is navigating growth prospects despite current challenges. The company expects revenue growth of 14% annually, outpacing the Australian market's 6% average, with earnings forecast to surge approximately 73% annually over the next three years. Despite reporting a net loss increase to $21.82 million in FY2025 from $20.01 million previously, Appen projects revenues between $270 million and $300 million for FY2026. The company maintains focus on research and development investments to remain competitive in the AI and data annotation sector. With a market capitalisation of A$416.77 million, Appen generates revenue through its China and Global segments, contributing $104.11 million and $127.87 million respectively.
Business leader of the week: CEO Andrew Ettinger to reinvent Hume AI. March 13, 2026 Hume AI CEO Andrew Ettinger will be responsible for accelerating the tech company's momentum in research services International Finance Business Desk Hume AI, the leading voice AI research company dedicated to aligning artificial intelligence with human well-being, recently announced a new CEO. The new boss, Andrew Ettinger, who has 15 years of leadership in data and AI infrastructure, building and scaling teams responsible for over USD 2 billion in ARR (Annual Recurring Revenue) at companies like Pivotal, Astronomer, and Appen, will now accelerate the tech company's momentum in research services. Andrew Ettinger recently served as Chief Revenue Officer at Appen, where he led commercial operations supplying hyperscalers and AI labs with proprietary datasets and LLM evaluation software. Appen is a leading AI data collection company that delivers high-quality, custom data across all languages and modalities (text, image, audio, and video) to create tailored datasets for training diverse AI models. New York-based data company Astronomer specialises in DataOps and AI orchestration. The company's flagship platform, Astro, allows businesses to build, manage, and scale complex data pipelines and AI workflows. Reacting to the news of his hiring, Ettinger said, "Voice in AI is evolving from a feature to the primary interface for the next generation of applications and devices. Understanding emotion will be essential to unlocking AI's full potential, and that will require ongoing systems that incorporate human-in-the-loop feedback. That's where Hume AI's data, annotation, and reinforcement-learning infrastructure is setting the pace for the industry." Hume AI recently agreed to license certain technologies non-exclusively to Google. Additionally, co-founder Alan Cowen has joined the company led by Sundar Pichai. Tough Test Awaits Andrew Ettinger Andrew Ettinger has a rich portfolio of guiding data and AI infrastructure-related companies, and his background is rooted in scaling enterprises. He studied Business Marketing at The Ohio State University. Rather than focusing on engineering or academia, Ettinger has leaned into growth, revenue, and, most importantly, figuring out how to take emerging technologies and turn them into sustainable businesses. Over the years, Andrew Ettinger has developed a reputation for helping startups move from early traction to serious revenue scale. A significant part of that was developing go-to-market strategies, building sales teams, establishing customer success structures, forging partnerships, and addressing the operational side that often determines whether a tech company can sustain its momentum beyond its early stages. One of Andrew Ettinger's more visible roles was at Pivotal Software, where he was involved during a high-growth phase. The company expanded rapidly, and Ettinger played a part in scaling revenue significantly before its IPO. Later, at Astronomer, he worked in global sales leadership, helping expand enterprise adoption of data and open-source tools. The roles at Pivotal Software and Astronomer helped Ettinger master the art of commercialising complex technical products for large customers. As already mentioned, Andrew Ettinger served as Chief Revenue Officer at Appen before becoming CEO at Hume AI. Appen provides data and evaluation services used to train and improve machine learning systems. That role put him right in the middle of the AI infrastructure world, working with major labs and technology companies, and gave Hume AI's new CEO direct exposure to how modern AI products are built and deployed. Hume AI, in the coming months, will be eyeing a fresh restart, as its previous CEO, Alan Cowen, along with several of the top engineers, got snapped up by Google in January 2026 in another incident of talent poaching, where promising individuals from small AI startups are being "inducted" into the fold of global tech titans. Cowen and his former Hume AI colleagues will now work with DeepMind to improve Gemini's voice features, as per WIRED. While Hume AI will continue to supply its technology to other AI firms, Andrew Ettinger, who joined the company a couple of weeks back before being promoted as the CEO, told TechCrunch that Google has a "non-exclusive right to certain technologies, and we'll be infusing that into their processes." According to reports, his immediate priority will be to release new models in the coming months and set up Hume AI to bring in USD 100 million in revenue this year. Hume AI, to some extent, has become a victim of the new trend called "acqui-hire," where tech biggies poach top AI talent (including startups' teams) to stay ahead of the innovation curve, while skirting regulatory scrutiny by acquiring a startup's talented individuals rather than the company outright. In 2025, Google followed the same template by acquiring viral AI coding startup Windsurf's CEO and other top researchers. OpenAI, which itself started as a non-profit research lab in 2015, has been a prominent practitioner of acqui-hire, bringing in several startup teams in recent months, including Convogo and Roi. Hume AI, which dubs its model as the "World's Most Realistic and Expressive Voice AI," has customised the tool to understand a user's emotions and mood based on their voice. In 2024, the startup launched its "Empathetic Voice Interface," a conversational AI with emotional intelligence. The company has raised funding close to USD 80 million to date, according to PitchBook. It only made sense for Google, which has been steadily improving its Gemini Live feature, which allows a user to have conversations with the chatbot, to go after Alan Cowen and his colleagues to refine the tech giant's product further and beat the industry competition.