About this role
Builders who enjoy close-knit in-person collaboration and want to shape foundational platform infrastructure will thrive on the Statsig team at OpenAI. Engineers on this team build the experimentation, feature rollout, dynamic configuration, and analytics systems that help OpenAI ship products with speed, safety, and evidence. This machine learning engineer will lead the technical direction for ML-powered experimentation and insights capabilities, working on an end-to-end, 0-to-1 initiative to turn learning and decision problems into general platform capabilities.
The work sits on the critical path for how product, engineering, research, and go-to-market teams learn from real-world usage across ChatGPT, Codex, and shared infrastructure. Responsibilities include building production systems that learn from privacy-protected product and experimentation data to generate evidence-backed insights and support decision-making. The ideal candidate has led ambiguous zero-to-one production machine learning products, possesses strong software engineering fundamentals in Python, and understands the boundary between predictive accuracy and causal validity.
What you will do
- Set and execute the technical roadmap for Generative Insights and Predictive Experimentation from early prototypes through production adoption.
- Build cross-experiment learning systems that retrieve and synthesize historical experiments and generate hypotheses with clear provenance.
- Develop predictive models and simulation workflows to estimate likely impact, affected segments, regression risk, and uncertainty before live tests.
- Create high-quality datasets and feature or retrieval pipelines with strong lineage, freshness, privacy, and data-quality controls.
- Establish rigorous evaluation through offline benchmarks, backtests, calibration, drift monitoring, and explicit failure or abstention behavior.
- Turn models into durable product, API, and agent workflows that move from an insight to experiment design and measured learning.
- Partner closely with data science and product teams on experiment design, causal inference, sequential decision-making, and variance reduction.
What you need
- Have led ambiguous 0-to-1 production ML products where success was measured by better real-world decisions rather than offline model metrics alone.
- Bring strong hands-on experience across the machine learning lifecycle including dataset design, training, evaluation, deployment, and monitoring.
- Demonstrate depth in one or more areas such as LLM and retrieval systems, ranking, forecasting, causal ML, or simulation.
- Possess strong software engineering fundamentals and the ability to build production systems in Python across data and backend boundaries.
- Have a strong grounding in machine learning, statistics, computer science, or a related field through formal study or practical experience.
- Understand experimentation and statistical reasoning, particularly the distinction between predictive accuracy and causal validity.
Benefits
- Offers Equity
Skills and tech stack
Questions candidates ask
What is the location and work policy for this role?
This role is based out of OpenAI's Bellevue office in Washington. The team is close-knit and works in person to move quickly and solve ambiguous problems together.
What is the salary range for the position?
The salary range for the position is $437K to $485K alongside offered equity.
What kind of systems will I be building?
You will build production systems that learn from privacy-protected product and experimentation data to generate evidence-backed insights, support decision-making, and help teams decide which ideas are worth testing live.