Chaos Labs is building a new AI platform for the enterprise, focused on how organizations operate and create value in an AI-native world. We’re bringing years of experience building applied AI and mission-critical infrastructure to a new category of enterprise software. Previously, Chaos Labs built the leading risk management platform for on-chain finance, processing more than $5 trillion in financial volume and managing tens of billions of dollars in assets. We’ve raised $65 million from Haun Ventures, Lightspeed, Galaxy, General Catalyst, Bessemer, F-Prime, PayPal, and others. We’re currently operating in stealth and building with a small team around a technically ambitious problem we believe will become increasingly important as AI reshapes how companies work.
How We Work
Chaos is built around high-agency, high-talent individual contributors. We keep teams small, give people meaningful ownership, and expect them to operate with autonomy, strong judgment, and a high bar for their craft. We hire people who can take an ambiguous, important problem and drive it forward — not just execute against a predefined roadmap. Titles matter less than impact, and the people closest to a problem are trusted to make decisions and own outcomes.We move fast, stay close to the work, and care deeply about building things exceptionally well. Ownership over instruction. Impact over hierarchy.
The Role
We’re hiring a Senior Data Scientist, Applied AI to own the development and evaluation of machine learning systems that power our enterprise AI platform. This is a high-impact role with significant ownership from day one. You’ll work closely with the Chief Science Officer, and Engineering to build and deploy production ML and LLM-powered capabilities while using rigorous experimentation, evaluation, and product analytics to continuously improve their quality, user impact, and business outcomes., taking problems from ambiguity through execution and measurable impact.
What You'll Do
- Design and build machine learning systems that power enterprise search, retrieval, recommendations, and AI agents.
- Develop evaluation frameworks and metrics to measure the quality, performance, and business impact of LLM-powered applications.
- Build scalable data and ML pipelines that process and analyze large-scale AI interaction data.
- Apply statistical modeling, experimentation, and causal inference to guide product development and strategic decision-making.
- Collaborate closely with Product, Engineering, and Design to translate research and insights into production features.
- Analyze product usage and customer behavior to identify opportunities for improving AI adoption, productivity, and user experience.
- Prototype, evaluate, and deploy new AI capabilities using foundation models, embeddings, and retrieval-augmented generation (RAG) techniques.
- Define key performance indicators and build analytical frameworks that inform product strategy and company-wide decisions.
- Contribute to the technical direction of our AI platform by identifying new opportunities to leverage machine learning and generative AI.
- Identify opportunities, form strong points of view, and turn them into action.
Requirements
- 5+ years of experience in Data Science, Applied Machine Learning, or a quantitative research role.
- A degree in Computer Science, Statistics, Mathematics, Machine Learning, Economics, Physics, or another highly quantitative field.
- A track record of developing and deploying machine learning models in production and turning ambiguous problems into measurable solutions.
- Strong proficiency in Python and SQL, with experience building scalable data and ML pipelines.
- Deep understanding of statistics, experimentation, causal inference, and predictive modeling.
- Experience working with large-scale datasets and modern data infrastructure.
- Familiarity with LLMs, embeddings, retrieval systems, RAG, or AI agents.
- Strong analytical and technical judgment, with the ability to translate ambiguous product and business questions into rigorous analytical or machine learning approaches.
- Excellent communication skills and the ability to work cross-functionally with Engineering, Product, Design, and Leadership.
- Comfort operating in ambiguity and taking ownership without waiting for direction.
- Exceptional standards for your own work and the ability to move quickly without sacrificing quality.
Preferred Qualifications
- Experience building or evaluating LLM-powered products or AI applications.
- Background in search, recommendation systems, information retrieval, or knowledge graphs.
- Experience designing A/B tests, offline evaluations, and ML benchmarking frameworks.
- Familiarity with vector databases, embedding models, and agent orchestration frameworks.
- Contributions to open-source ML projects or publications in machine learning, NLP, or related fields.
- Experience in B2B SaaS, developer tools, or enterprise AI products.
- Flexibility to work remotely. Ability to travel up to 30% of the year to collaborate in person with the team.
Benefits
- Compensation & Equity – competitive package aligned with growth, performance, and merit
- Career Growth Opportunities – be part of a rapidly expanding, global technology company with room to grow professionally