2 months ago
Base Salary
$295k - $445k/yr
Responsibilities
- Partner with strategic customers and internal teams to define target model behaviors, diagnose failure modes, and translate 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 assessing training and customization outcomes.
- Improve data, evaluation, training, infrastructure, tooling, APIs, and developer workflows based on deployment learnings.
- Integrate ML capabilities into AWS-native API environments with backend and infrastructure engineers.
- Collaborate with Research, Applied, Safety Systems, infrastructure teams, and external technical partners to bring model improvements and evaluation practices into production.
- Design systems that enable safe model customization for strategic partners and enterprise customers.
- Debug and improve systems spanning model behavior, training data, APIs, distributed infrastructure, and customer-facing product surfaces.
- Own ambiguous 0→1 problems while improving system reliability, scalability, and effectiveness.
Requirements
- Master's or PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
- At least 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 such as PyTorch or TensorFlow.
- Experience training and fine-tuning large language models using supervised fine-tuning, distillation, preference optimization, reinforcement learning, or other post-training techniques.
- Strong software engineering fundamentals in data structures, algorithms, systems design, and production code using 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 operating with ambiguity, taking end-to-end ownership, and learning new areas as needed.
Categories
About OpenAI
OpenAI builds and deploys large-scale AI models and tools—including ChatGPT, GPT-4–class models, DALL·E, and Whisper—sold via APIs and enterprise subscriptions to developers and businesses. It monetizes through usage-based API pricing and ChatGPT Plus/Team/Enterprise, and also reaches customers via Microsoft’s Azure OpenAI Service. Founded in 2015 and headquartered in San Francisco, it operates as a private partnership.