Machine Learning Engineer, Core Experimentation

📋 Job Overview

The Statsig team within OpenAI builds the experimentation, feature rollout, dynamic configuration, and analytics systems that help OpenAI ship products with speed, safety, and evidence. Our work sits on the critical path for how product, engineering, research, and go-to-market teams learn from real-world usage and make high-confidence decisions.

🏢 About OpenAI

Statsig began as an independent company focused on helping builders move faster through trustworthy experimentation and feature management. After Statsig joined OpenAI, the team began the next chapter: bringing that deep product expertise, customer intuition, and mature platform infrastructure into OpenAI as the experimentation and rollout platform for every product we ship.

🎯 The Role

We are looking for a Machine Learning Engineer to lead the technical direction for ML-powered experimentation and insights capabilities. You will build production systems that learn from privacy-protected product and experimentation data to generate evidence-backed insights and support decision-making, and help teams decide which ideas are worth testing live.

✅ Key Responsibilities

• 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, detect recurring effects and segment behavior, reanalyze prior results when data or methods improve, and generate hypotheses with clear evidence and provenance.
• Develop predictive models and simulation workflows, including simulation-based evaluation approaches, to estimate likely impact, affected segments, regression risk, and uncertainty before a full live experiment.
• Create high-quality, production-ready ML systems that are robust, scalable, and maintainable.
• Collaborate closely with product, engineering, and research teams to understand their learning and decision problems and translate them into technical requirements and platform capabilities.
• Design systems that make uncertainty explicit, backtest against historical outcomes, compare predictions with online results, learn from misses, and abstain when the evidence is weak.
• Preserve clear review, permission, and approval boundaries as automation becomes more powerful.

🎁 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, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, 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 to fuel your professional growth
Daily meals in our offices, and meal delivery credits as eligible
Relocation support for eligible employees
Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.