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 is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. AI is an extremely powerful tool that must be created with safety and human needs at its core. OpenAI is dedicated to putting that alignment of interests first — ahead of profit. To achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. Our investment in diversity, equity, and inclusion is ongoing, executed through a wide range of initiatives, and championed and supported by leadership. At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.