Machine Learning Engineer, Core Experimentation
Seattle · Full-time · Hybrid · Posted Sep 24, 2026
At a glance
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- Seattle
About OpenAI
- OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
- We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
- For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf.
- Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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- We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg&d=57018692298241.
About 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.
- This is an end-to-end, 0-to-1 role working across ML modeling, retrieval and LLM systems, statistical methods, simulation, data and training pipelines, backend services, and product experiences.
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 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 rigorous evaluation through 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 move from an insight to experiment design, approval-gated action, and measured learning.
- Partner deeply 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 so sophisticated ML capabilities are understandable and useful to teams making high-stakes product decisions.
- Provide technical leadership across engineering, product, data science, and research partners, and raise the bar for production ML quality across the platform.
Requirements
- Have led ambiguous 0-to-1 production ML products where success was measured by better real-world decisions, not only offline model metrics.
- Have strong hands-on experience across the ML lifecycle: dataset design, training or adaptation, evaluation, deployment, monitoring, and iteration.
- Bring depth in one or more of LLM and retrieval systems, ranking or recommendation, forecasting or anomaly detection, causal ML or experiment analysis, or simulation.
- Have strong software engineering fundamentals and can build high-quality production systems in Python while working comfortably across data, backend, and platform boundaries.
- Have a strong grounding in machine learning, statistics, computer science, or a related field through formal study or equivalent practical experience.
- Understand 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, not cleanup work.
- Can translate ambiguous partner questions into a product and technical roadmap, and work well with product, data science, research, and infrastructure partners.
Workplace
- The role is based in Bellevue, Washington.
Skills
- Python
- LLM Systems
- Retrieval Systems
- Machine Learning
- Statistical Methods
- Simulation
- Causal ML
- Forecasting
- Anomaly Detection
