Machine Learning Engineer, API Multicloud

About the Team

OpenAIs API Multicloud team sits within B2B Applications and is responsible for extending OpenAIs API platform into strategic cloud environments starting with AWS. The teams mission is to distribute OpenAIs API broadly and safely by enabling key API technologies in AWS-native environments in close partnership with Amazon and internal teams across Codex Research Safety Systems and Applied.

The team is focused on bringing core developer and enterprise capabilities into cloud-native environments including AWS-hosted Codex model customization / post-training as a service and new stateful runtime environments for agentic workloads. This work sits at the intersection of production ML systems developer platforms model behavior and large-scale infrastructure.

About the Role

Were hiring Machine Learning Engineers to build and improve the AI systems that help strategic partners adapt OpenAI models to important use cases in cloud-native environments. This role spans post-training workflows evaluation data pipelines model behavior and API/infrastructure integration.

Youll work at the boundary between partner needs and core ML systems: helping teams understand what is and isnt working diagnosing issues in training and evaluation workflows and turning those learnings into improvements to the underlying platform. Youll collaborate closely with Research Applied Safety Systems infrastructure teams and external technical partners to solve ambiguous model-performance problems. When you succeed strategic partners and internal teams will be able to improve model behavior with confidence driving measurable product improvements while the systems behind that work become more reliable scalable and effective over time.

In this role you will

• 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 the 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 01 environment where requirements are ambiguous systems are evolving quickly and reliability matters.

Your background might look something like:

• Masters or PhD in Computer Science Machine Learning or a 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 methods like supervised fine-tuning distillation preference optimization reinforcement learning or other post-training techniques.

• Strong software engineering fundamentals including data structures algorithms systems design and high-quality 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 while collaborating closely with Research Safety product engineering infrastructure and external technical partners.

• Comfort moving quickly through ambiguity owning problems end-to-end and learning whatever is needed to get the job done.

• Bonus: experience with AWS Kubernetes agents tool use runtime environments AI developer platforms or speech models.

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 OpenAIs Affirmative Action and Equal Employment Opportunity Policy Statement.

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 addition job duties require access to secure and protected information technology systems and related data security obligations.

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IC