Machine Learning Engineer, Core Experimentation

OpenAI’s Statsig team is building the experimentation and analytics foundation that helps the company ship new product capabilities with speed, safety, and evidence. In this Machine Learning Engineer role, you will guide the technical direction for ML-powered experimentation and insights, shaping production systems that produce traceable, calibrated, privacy-protected evidence to support high-stakes decisions.

You will work end-to-end, from early prototypes through production adoption, helping teams move from model predictions to experimentation-backed understanding. Live experiments remain the source of causal validation, and the systems you create will make uncertainty explicit while supporting review, permissions, and approval boundaries as automation increases.

Role focus

• Set and execute the technical roadmap for Generative Insights and Predictive Experimentation, moving from 0-to-1 prototypes to production use.
• Build cross-experiment learning systems that retrieve and synthesize historical experiments, detect recurring effects and segment behavior, and reanalyze prior results when data or methods improve.
• Generate hypotheses with clear evidence and provenance, connecting offline insights to what teams can verify in live experiments.
• Develop predictive models and simulation workflows to estimate likely impact, affected segments, regression risk, and uncertainty before running full live experiments.
• Create high-quality datasets and feature or retrieval pipelines using exposures, events, metrics, experiment metadata, and replay data, with strong lineage, freshness, privacy, and data-quality controls.
• Establish rigorous evaluation using offline benchmarks, backtests, calibration, drift monitoring, prediction-to-outcome comparisons, and explicit failure or abstention behavior.
• Turn models into durable product, API, and agent workflows that connect insight generation to experiment design, approval-gated action, and measured learning.
• Partner closely with data science and product teams on experiment design, causal inference, sequential decision-making, variance reduction, and the boundary between prediction and causal evidence.
• Build reliable services and intuitive workflows that help teams interpret sophisticated ML capabilities for real-world product decisions.
• Provide technical leadership across engineering.

Requirements

• Experience leading ambiguous 0-to-1 production ML products where success is measured by better real-world decisions, not only offline metrics.
• Hands-on capability across the ML lifecycle: dataset design, training or adaptation, evaluation, deployment, monitoring, and iteration.
• Depth in one or more areas including LLM and retrieval systems, ranking or recommendation, forecasting or anomaly detection, causal ML or experiment analysis, or simulation.
• Strong software engineering fundamentals and ability to build high-quality production systems in Python while working comfortably across data boundaries.
• Strong grounding in machine learning study or equivalent practical experience.
• Understanding of experimentation and statistical reasoning, especially why predictive accuracy is not the same as causal validity.
• Treat calibration, uncertainty, provenance, privacy, and human review as product requirements.
• Ability to translate ambiguous partner questions into a product and technical roadmap, collaborating well across product, data science, research, and infrastructure.
• Comfort building for internal power users and agents, making sophisticated ML capabilities clear and actionable.
• Value in-person collaboration and interest in helping shape a growing Bellevue-based team.

Technologies

• Python
• LLM
• Retrieval systems

About the team

• The Statsig team within OpenAI builds experimentation, feature rollout, dynamic configuration, and analytics systems to help OpenAI ship products with speed, safety, and evidence.
• Statsig began as an independent company focused on trustworthy experimentation and feature management.
• Today, the team supports capabilities across ChatGPT, Codex, model measurement, consumer experiences, business subscriptions, developer products, and shared infrastructure that connects them.
• Teams rely on Statsig to safely introduce new capabilities, compare product and model behavior, measure impact, and roll changes forward or back with confidence.
• The role is on the critical path for how product, engineering, research, and go-to-market teams learn from real-world usage and make high-confidence decisions.

Location and workplace

• Based in Seattle, WA (onsite).
• The team works in person to move quickly, solve ambiguous problems together, and stay close to the product teams it supports.

Compensation

• $437K to $485K per year + Offers Equity.
• Base pay may vary based on individualized factors, and additional compensation and benefits may apply for eligible employees.

Benefits

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts.
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit).
• 401(k) retirement plan with employer match.
• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks).
• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees.
• 13+ paid company holidays, plus multiple paid coordinated company office closures throughout the year for focus and recharge, and paid sick or safe time as required by applicable state or local law.
• Mental health and wellness support.
• Employer-paid basic life and disability coverage.
• Annual learning and development stipend.
• Daily meals in offices, and meal delivery credits as eligible.
• Relocation support for eligible employees.
• Additional taxable fringe benefits may be provided, including charitable donation matching and wellness stipends.