Role OverviewWe are looking for an LLM Engineer to design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches.
What You Will Do
Design and execute fine-tuning experiments, lead dataset construction, build scalable training pipelines, and implement parameter-efficient fine-tuning techniques.
Why It Might Be a Fit
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.
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.
- Track record of shipping or publishing impactful LLM work.
Benefits
- Dental insurance
- Vision insurance
- Health insurance
- Paid time off
- Retirement plan
- Stock options
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