Generative AI Engineer

Bright Vision Technologies•Sunnyvale, CA

3d•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking a Generative AI Engineer to design, execute, and operationalize fine-tuning workflows for large language models using supervised, preference-based, and reinforcement learning approaches. This 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 will combine strong ML intuition with production-grade engineering practices, balancing data quality, compute budget, evaluation rigor, and shipping velocity. You will collaborate with cross-functional partners (product, design, engineering, operations, and business stakeholders) to translate ambiguous requirements into well-engineered solutions. You are expected to elevate the team through code reviews, design reviews, and mentorship of junior engineers. The successful candidate possesses strong engineering discipline, clear communication skills, and a proven history of delivering impactful production-ready work.

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.

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.
Utilize supervised, preference-based, and reinforcement learning approaches for model fine-tuning.
Construct datasets and implement rigorous evaluation methodologies.
Operate complex training pipelines reliably.
Collaborate with cross-functional partners to translate requirements into solutions.
Conduct code reviews and design reviews.
Mentor more junior engineers.