Machine Learning Engineer, API Multicloud
San Francisco • FullTime
Posted 1mo ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
On-site
Employment Type
FullTime
Categories
Machine Learning Engineer
About the job
OpenAI is seeking Machine Learning Engineers to join its API Multicloud team, focusing on extending OpenAI's API platform into strategic cloud environments, starting with AWS. This role involves building and improving AI systems that help strategic partners adapt OpenAI models for cloud-native use cases. You will operate at the intersection of production ML systems, developer platforms, model behavior, and large-scale infrastructure, spanning post-training workflows, evaluation, data pipelines, and API/infrastructure integration. The ideal candidate will enjoy working with external technical partners, diagnosing issues, and translating learnings into platform improvements, collaborating closely with Research, Applied, Safety Systems, and infrastructure teams.
Responsibilities
- 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.
- Design, run, and interpret experiments to assess training and customization workflow outcomes and identify performance improvements.
- Partner with backend and infrastructure engineers to integrate ML capabilities into AWS-native API environments.
- Propose and implement improvements to post-training systems, tooling, APIs, and developer workflows based on partner deployment learnings.
- Collaborate with Research and Applied teams to integrate model improvements, training workflows, and evaluation best practices into production.
- Help design systems for safe customization of OpenAI models by strategic partners and enterprise customers.
- Debug and improve complex systems involving model behavior, training data, APIs, distributed infrastructure, and customer-facing product surfaces.
- Operate with high ownership in a 0→1 environment with ambiguous requirements and rapidly evolving systems.
Requirements
- Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
- 3+ 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.
- Hands-on experience with deep learning, transformer models, and frameworks like PyTorch or TensorFlow.
- Hands-on experience with training and fine-tuning large language models, including methods like supervised fine-tuning, distillation, preference optimization, or reinforcement learning.
- Strong software engineering fundamentals, including data structures, algorithms, systems design, and production code 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, collaborating with various teams and external partners.
- Comfort moving quickly through ambiguity, owning problems end-to-end, and learning as needed.