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.

Responsibilities

• Work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions
• Raise the bar through code review, design review, and mentorship of more junior engineers
• Ship meaningful work that holds up well in production

Required Qualifications

• 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
• Track record of shipping or publishing impactful LLM work

Preferred Qualifications

• Publications at top-tier ML venues
• Experience with multimodal model fine-tuning
• Familiarity with synthetic data generation and dataset distillation
• Open-source contributions to LLM training libraries
• Exposure to responsible AI evaluation and red-teaming practices

Pay

$90,000–$115,000 annually

Schedule

Full-time