Foundation Model Engineer
About The Position
Bright Vision Technologies is seeking a Foundation Model Engineer to design, execute, and operationalize fine-tuning workflows for large language models using supervised, preference-based, and reinforcement learning approaches. The role involves modern training stacks, dataset construction, rigorous evaluation, and reliable operation of complex training pipelines. The engineer will balance data quality, compute budget, evaluation rigor, and delivery speed while applying production-grade engineering practices. The position works closely with product, design, engineering, operations, and business stakeholders to translate ambiguous requirements into well-engineered solutions. The successful candidate will contribute to code and design reviews, mentor more junior engineers, communicate clearly, and have a track record of delivering impactful work that performs well in production.
Requirements
- 10 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.
Nice To Haves
- 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.
Responsibilities
- Design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches.
- Construct datasets and apply rigorous evaluation methodology to training workflows.
- Operate complex training pipelines reliably, including training jobs on GPU clusters and recovery from failures.
- Balance data quality, compute budget, evaluation rigor, and delivery speed.
- Work with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into well-engineered solutions.
- Contribute to code reviews and design reviews.
- Mentor more junior engineers.
- Ship or publish impactful LLM work.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
