# Machine Learning Engineer, API Multicloud

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $295k – $445k/yr
**Experience:** 7+ years
**Skills:** Python, Rust, PyTorch, TensorFlow, Deep Learning, transformer models, LLMs, supervised fine-tuning, Reinforcement Learning, Kubernetes, AWS, Distributed Systems, Data Pipelines, Model Evaluation, Cloud Infrastructure
**Posted:** 2026-08-13

> 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.

## Job Description

## 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.

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**Apply:** https://hotfix.jobs/jobs/e00aaa80-2064-4ba0-9c1b-993a835011d2
**Canonical:** https://hotfix.jobs/jobs/e00aaa80-2064-4ba0-9c1b-993a835011d2