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OpenAIOpenAISan Francisco, CA

Machine Learning Engineer, API Multicloud

Build production machine learning systems for model customization, post-training, evaluation, and AWS-native API integration. The role requires 7+ years of relevant engineering experience and expertise in deep learning, transformers, LLM fine-tuning, and production ML infrastructure.

295k – 445k/yr
On-site7+ YOEML Engineering

About the role

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.
  • Investigate whether training and customization workflows produce 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.
  • Improve post-training systems, tooling, APIs, and developer workflows based on partner deployment learnings.
  • Bring model improvements, training workflows, and evaluation best practices into production.
  • Design systems that enable strategic partners and enterprise customers to safely customize OpenAI models.
  • Debug and improve systems spanning model behavior, training data, APIs, distributed infrastructure, and customer-facing product surfaces.
  • Operate with high ownership in an ambiguous 0→1 environment where reliability matters.

Requirements

  • Master’s 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.
  • Hands-on experience with deep learning, transformer models, and frameworks such as 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, 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 collaborate across model behavior, APIs, infrastructure, Research, Safety, product engineering, and external technical partners.
  • Comfort moving quickly through ambiguity, owning problems end-to-end, and learning as needed.

Nice-to-haves

  • AWS
  • Kubernetes
  • Agents
  • Tool use
  • Runtime environments
  • AI developer platforms
  • Speech models

Compensation

  • Annual salary range: $295,000–$445,000.

Skills

PythonRustPyTorchTensorFlowDeep Learningtransformer modelsLLMssupervised fine-tuningReinforcement LearningKubernetesAWSDistributed SystemsData PipelinesModel EvaluationCloud Infrastructure

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