Machine Learning Engineer, Core Experimentation

OpenAI

. Set and execute the technical roadmap for Generative Insights and Predictive Experimentation from prototypes through production adoption .

Posted 9/24/2026full-timeBellevue • Washington • United StatesMid-LevelSenior💰 $437,000 - $485,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading the development and execution of machine learning products, with a strong focus on predictive modeling, experimentation, and cross-functional collaboration. Proficient in building high-quality production systems and translating complex partner needs into actionable technical roadmaps.

Highest-signal resume keywords

Machine Learning Lifecycle ManagementPredictive Modeling and SimulationPython Development for Production SystemsCausal Inference and Experiment AnalysisTechnical Leadership and Collaboration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPredictive ModelingDataset DesignExperimentationStatistical ReasoningCausal MLSimulation WorkflowsEvaluation and MonitoringSoftware EngineeringData Pipeline Development

Soft Skills

CollaborationProblem-SolvingCommunicationAdaptabilityLeadership

Tools & Technologies

APIsData Science ToolsExperimentation PlatformsStatistical SoftwareMachine Learning Frameworks

Industry Keywords

Generative InsightsPredictive ExperimentationCausal ValidityUncertainty CalibrationHuman Review

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Set and execute the technical roadmap for Generative Insights and Predictive Experimentation 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 evidence-backed hypotheses
Develop predictive models and simulation workflows to estimate likely impact, affected segments, regression risk, and uncertainty before live experiments
Create datasets and feature or retrieval pipelines from exposures, events, metrics, experiment metadata, and replay data
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 supporting 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 prediction versus causal evidence
Build reliable services and intuitive workflows for high-stakes product decisions
Provide technical leadership across engineering, product, data science, and research partners
Raise production ML quality across the experimentation platform
Collaborate with teams building ChatGPT, Codex, model measurement workflows, consumer products, Growth, business subscription experiences, developer products, and shared infrastructure

Requirements

What you’ll need

Experience leading ambiguous 0-to-1 production ML products measured by improved real-world decisions
Strong hands-on experience across the ML lifecycle: 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
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
Understanding of calibration, uncertainty, provenance, privacy, and human review as product requirements
Ability to translate ambiguous partner questions into a product and technical roadmap
Ability to work with product, data science, research, and infrastructure partners
Interest in building for internal power users and agents
Value in-person collaboration and willingness to help shape a Bellevue-based team
Ability to work from the US office three days per week

Benefits

Comp & perks

Equity
Performance-related bonus(es) for eligible employees
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)
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
Multiple paid coordinated company office closures throughout the year for focus and recharge
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
Daily meals in 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