Senior Generative AI Engineer — Remote & LLM Fine-Tuning

Bright Vision Technologies; United States; Remote; USD 90,000 - 115,000; Full time

Job summary

Bright Vision Technologies is seeking a Generative AI Engineer to design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires strong ML intuition, engineering discipline, and production-ready pipelines.

You will collaborate with product, design, and engineering teams to translate ambiguous requirements into robust solutions, balance data quality with compute budgets, and mentor

Qualifications

• 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 PyTorch.

Responsibilities

• Design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches.
• Collaborate with cross-functional partners to translate ambiguous requirements into well-engineered solutions.
• Maintain production-grade training pipelines and ensure reliable operation of training jobs on GPU clusters.
• Mentor junior engineers and contribute to design and code reviews.

Skills

Strong communication

Production-grade engineering

Shipping impactful LLM work

Python proficiency

English writing

Education

Master's or PhD in CS/ML or equivalent

Tools

Python

PyTorch

FSDP

ZeRO

Pipeline parallelism

RLHF/DPO

Job description

Bright Vision Technologies is seeking a Generative AI Engineer to design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires strong ML intuition, engineering discipline, and production-ready pipelines.

You will collaborate with product, design, and engineering teams to translate ambiguous requirements into robust solutions, balance data quality with compute budgets, and mentor