Foundation Model Engineer

About the role

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. The position is 100% Remote (U.S.), full-time, Direct W2.

Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. The company is unable to sponsor new H-1B visa petitions for this position.

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, 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.
• Operate complex training pipelines reliably, including dataset construction, evaluation methodology, and recovery from failures on GPU clusters.

Requirements

• 10+ 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.

Qualifications

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

Pay

Salary Range: $200,000–$230,000 Annually.

Schedule

Full‑time, Direct W2.