Machine Learning Engineer, API Multicloud
About the Team OpenAI’s API Multicloud team sits within B2B Applications and is responsible for extending OpenAI’s API platform into strategic cloud environments, starting with AWS. The team’s mission is to distribute OpenAI’s API broadly and safely by enabling key API technologies in AWS-native environments, in close
What this role actually needs.
About the Team OpenAI’s API Multicloud team sits within B2B Applications and is responsible for extending OpenAI’s API platform into strategic cloud environments, starting with AWS. The team’s mission is to distribute OpenAI’s API broadly and safely by enabling key API technologies in AWS-native environments, in close 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. Company context: OpenAI builds frontier AI systems, research infrastructure, and applied products for developers, enterprises, and global users.
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- 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.
Why this listing is more than a copied job post.
Machine Learning Engineer, API Multicloud is framed against UpJobz source checks, country scope, compensation visibility, and work-authorization signals so candidates can make a faster go/no-go decision.
United States tech market
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Compensation read
$295K - $445K is visible before the click, so candidates can compare the role against local market expectations before applying.
Work authorization read
Current extracted signal: United States residents. UpJobz treats this as a search signal, not legal advice, and links visa-sensitive roles back to the relevant visa hub where possible.
Location read
On-site roles in San Francisco should be compared against commute, local salary bands, and nearby employer demand.
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Next moves
- Tailor your resume around ai and llm instead of sending a generic application.
- Use the first two bullets of your application to connect your background directly to machine learning engineer, api multicloud is a high-signal on-site role in san francisco, and it is most realistic for united states residents.
- Open the role quickly if it fits and bookmark three similar jobs before you leave the page.
Interview themes
Watchouts
- $295K - $445K is visible, so calibrate your application around the posted range.
- Use united states residents as part of your positioning so the recruiter does not have to infer it.
- Show concrete examples of succeeding in on-site environments.
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Source: jobs.ashbyhq.com · Source ID: 5acf4854-1d42-40ca-bff8-4f6f04cdce68 · Confidence: 97/100 · Last checked: May 7, 2026
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