Foundation Model Engineer

Bright Vision Technologies • Wildwood, MO

22h • $200,000 - $230,000 • Remote

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 working with modern training stacks, constructing high-quality datasets, developing rigorous evaluation methods, and reliably operating complex training pipelines. The engineer will balance data quality, compute budgets, evaluation rigor, and delivery speed while applying production-grade engineering practices.

The engineer will collaborate with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into well-engineered solutions. The role also includes contributing to code and design reviews and mentoring more junior engineers. Candidates should bring strong ML intuition, clear communication, engineering discipline, and a record of delivering impactful work that performs well in production.

This is a full-time, direct W2 position that is 100% remote within the United States. The stated annual salary range is $200,000–$230,000. The employer encourages applications from U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates, but cannot sponsor new H-1B visa petitions for this position.

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.
• A 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 develop rigorous evaluation methodologies for LLM training and assessment.
• Operate complex training pipelines reliably, including training jobs on GPU clusters and recovery from failures.
• Balance data quality, compute budgets, evaluation rigor, and delivery speed.
• Collaborate with product, design, engineering, operations, and business stakeholders to translate ambiguous requirements into well-engineered solutions.
• Participate in code reviews and design reviews.
• Mentor more junior engineers.
• Ship impactful LLM work that holds up well in production.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed