Generative AI Engineer

About the role

We are looking for an Generative AI Engineer to design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires deep practical experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to operate complex training pipelines reliably. The ideal candidate combines strong ML intuition with production-grade engineering practices, and is comfortable navigating the trade-offs between data quality, compute budget, evaluation rigor, and shipping velocity. In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production.

Responsibilities

• Design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches.
• Work closely with cross-functional partners (product, design, engineering, operations, business) to translate ambiguous requirements into well-engineered solutions.
• Raise the bar through code review, design review, and mentorship of junior engineers.
• Operate complex training pipelines reliably on GPU clusters, including recovery from failures.
• Navigate trade-offs between data quality, compute budget, evaluation rigor, and shipping velocity.

Requirements

• Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience
• Six or more years of combined ML research and engineering experience, with significant LLM exposure
• Strong proficiency in Python and modern deep learning frameworks, especially PyTorch
• Hands-on experience fine-tuning transformer-based language models at non-trivial scale
• Familiarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelism
• Experience with RLHF, DPO, or other preference optimization techniques
• Strong understanding of evaluation methodology, benchmarks, and human evaluation design
• Experience operating training jobs on GPU clusters and recovering from failures
• Strong written and verbal communication skills