Machine Learning Engineer, Core Experimentation

Details

The Statsig team within OpenAI builds experimentation, feature rollout, dynamic configuration, and analytics systems that help teams ship products safely and make evidence-based decisions. The team supports products and workflows across ChatGPT, Codex, model measurement, consumer experiences, business subscriptions, developer products, and shared infrastructure.

Responsibilities

• Lead the technical direction and roadmap for ML-powered experimentation and insights capabilities, from prototypes through production adoption.
• Build cross-experiment learning systems that retrieve and synthesize historical experiments, detect recurring effects and segment behavior, reanalyze prior results, and generate hypotheses with clear evidence and provenance.
• Develop predictive models and simulation workflows to estimate likely impact, affected segments, regression risk, and uncertainty before full live experiments.
• Create datasets and feature or retrieval pipelines from exposures, events, metrics, experiment metadata, and replay data, with strong lineage, freshness, privacy, and data-quality controls.
• Establish evaluation through offline benchmarks, backtests, calibration, drift monitoring, prediction-to-outcome comparisons, and explicit failure or abstention behavior.
• Turn models into product, API, and agent workflows that support experiment design, approval-gated action, and measured learning.
• Partner with data science and product teams on experiment design, causal inference, sequential decision-making, variance reduction, and the distinction between prediction and causal evidence.
• Build reliable services and intuitive workflows for teams making high-stakes product decisions.
• Provide technical leadership across engineering, product, data science, and research partners, and raise production ML quality across the platform.

Requirements

• Experience leading ambiguous 0-to-1 production ML products measured by improved real-world decisions.
• Hands-on experience across the ML lifecycle, including dataset design, training or adaptation, evaluation, deployment, monitoring, and iteration.
• Depth in one or more of LLM and retrieval systems, ranking or recommendation, forecasting or anomaly detection, causal ML or experiment analysis, or simulation.
• Strong software engineering fundamentals and the ability to build high-quality production systems in Python across data, backend, and platform boundaries.
• Strong grounding in machine learning, statistics, computer science, or a related field through formal study or equivalent practical experience.
• Understanding of experimentation and statistical reasoning, including the distinction between predictive accuracy and causal validity.
• Commitment to calibration, uncertainty, provenance, privacy, and human review as product requirements.
• Ability to translate ambiguous partner questions into a product and technical roadmap and collaborate with product, data science, research, and infrastructure teams.
• Interest in building for internal power users and agents and making sophisticated ML capabilities clear and actionable.

Workplace

The role is based in Bellevue, Washington. The team works in person and values in-person collaboration.

Benefits

• Base salary of $437,000–$485,000 per year, plus equity.
• Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
• Pre-tax Flexible Spending Accounts and commuter benefits.
• 401(k) retirement plan with employer match.
• Paid parental, medical, and caregiver leave.
• Paid time off, paid company holidays, office closures, and sick or safe time.
• Mental health and wellness support.
• Employer-paid basic life and disability coverage.
• Annual learning and development stipend.
• Daily office meals and eligible meal delivery credits.
• Relocation support for eligible employees.
• Additional benefits may include charitable donation matching and wellness stipends.