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OpenAI's API Multicloud team is hiring Machine Learning Engineers to build and improve AI systems that help strategic partners adapt OpenAI models to important use cases in cloud-native environments, starting with AWS. This role spans post-training workflows, evaluation, data pipelines, model behavior, and API/infrastructure integration.
You'll work at the boundary between partner needs and core ML systems: helping teams understand what is and isn't working, diagnosing issues in training and evaluation workflows, and turning those learnings into improvements to the underlying platform. You'll collaborate closely with Research, Applied, Safety Systems, infrastructure teams, and external technical partners to solve ambiguous model-performance problems.
Key responsibilities include:
- Partner with strategic customers and internal teams to define target model behaviors, diagnose failure modes, and translate real-world needs into training, evaluation, and system requirements
- Build and scale production ML systems for model customization, post-training, and fine-tuning-as-a-service workflows
- Investigate whether training and customization workflows are producing intended outcomes, and identify changes to data, evaluation, training, or infrastructure that improve performance
- Partner with backend and infrastructure engineers to integrate ML capabilities into AWS-native API environments
- Feed learnings from partner deployments back into the platform by proposing and implementing improvements to post-training systems, tooling, APIs, and developer workflows
- Work closely with Research and Applied teams to bring model improvements, training workflows, and evaluation best practices into production
- Help design systems that allow strategic partners and enterprise customers to safely customize OpenAI models for high-value use cases
- Debug and improve complex systems spanning model behavior, training data, APIs, distributed infrastructure, and customer-facing product surfaces
- Operate with high ownership in a 0→1 environment where requirements are ambiguous, systems are evolving quickly, and reliability matters
Required background: Master's or PhD in Computer Science, Machine Learning, or related field (or equivalent practical experience); 7+ years of professional engineering experience in relevant ML, infrastructure, or product-driven engineering roles; strong ML engineering experience building, training, fine-tuning, evaluating, or deploying production AI systems with hands-on experience in deep learning, transformer models, and frameworks like PyTorch or TensorFlow; familiarity with training and fine-tuning large language models including supervised fine-tuning, distillation, preference optimization, reinforcement learning, or other post-training techniques; strong software engineering fundamentals in Python, Rust, or similar languages; experience with model customization, evaluation systems, data pipelines, distributed systems, cloud infrastructure, or production ML platform tradeoffs; ability to operate across model behavior, APIs, and infrastructure while collaborating with Research, Safety, product engineering, infrastructure, and external technical partners; comfort moving quickly through ambiguity and owning problems end-to-end. Bonus: AWS, Kubernetes, agents, tool use, runtime environments, AI developer platforms, or speech models experience.
