Machine Learning Engineer, Implicit Signals

📋 Job Overview

Our team builds classification and evaluation systems that give post-training and research teams fast, reliable feedback on model behavior. We combine machine learning and production engineering to make these capabilities accurate, efficient, and useful at scale, helping researchers assess changes and improve models and products. We work closely with research teams to develop useful measurements and make them practical to run. Our work spans classifier development, rigorous evaluation, and efficient production inference, with ownership from initial experimentation through deployment and continued improvement.

🏢 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.

🎯 The Role

We’re looking for a machine learning engineer to develop new classification and evaluation capabilities and bring them into production. You’ll help turn open-ended questions about model behavior into well-defined ML problems, build methods that address them, and establish how reliably those methods work. This role combines traditional machine learning, model evaluation, and real-time engineering. You’ll own classifiers throughout their lifecycle, including supervised fine-tuning, distillation, evaluation, deployment, and production inference. You’ll work closely with researchers to identify valuable problems and with software engineers to make promising approaches useful at scale.

✅ Key Responsibilities

• Build, deploy, and operate production classification and evaluation systems that provide useful feedback to post-training and research teams.
• Optimize production inference, including model serving, batching, caching, throughput, latency, compute utilization, and cost.
• Train and optimize classifiers using supervised fine-tuning and distillation, validating quality and efficiency before deployment.
• Own the reliability and observability of deployed classifiers, diagnose production issues, and improve their performance over time.
• Design rigorous evaluations that assess accuracy, calibration, generalization, and potential measurement bias while guiding production tradeoffs.
• Partner with software engineers to integrate models into reliable execution systems and support safe rollouts.
• Build reusable engineering tools that make classifier training, evaluation, deployment, and iteration faster and more repeatable.
• Work with post-training researchers and data scientists to translate their needs into modeling objectives, evaluation criteria, and production requirements.

📌 Required Qualifications

• Experience taking ML projects from problem formulation and experimentation through production deployment.
• Strong foundations in supervised learning, statistics, and experimental design.
• Practical experience evaluating models, investigating systematic errors, and working with imperfect labels.
• Strong software engineering skills and the ability to build maintainable production ML systems.
• Experience reasoning about inference performance and the tradeoffs between quality, latency, and compute cost.
• The ability to combine careful scientific judgment with a practical approach to shipping and operating software.
• Comfort working closely with researchers and engineers on open-ended problems.
• Fluency in using AI agents to move quickly across multiple workstreams in a fast-paced environment.

⭐ Desirable Experience

• Experience with model distillation, representation learning, calibration, or efficient inference is a plus.

🎁 Benefits

Base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. Total compensation includes:
Salary: $381K – $510K • Offers Equity
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.

🛂 Visa & Eligibility

This role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.