Senior Generative AI Engineer - Remote, Production ML

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

Job summary

Bright Vision Technologies is seeking a Generative AI Engineer for 100% remote work in the United States. You will design, implement, and operationalize fine-tuning workflows for large language models, focusing on practical production pipelines and robust evaluation.

The role requires 6+ years of ML research and engineering, strong Python and PyTorch skills, and experience with distributed training strategies and RLHF/DPO methods.

Qualifications

• Master’s or PhD in Computer Science, ML, or related field or equivalent experience.
• Six+ years of ML research and engineering experience with significant LLM exposure.
• Proficiency in Python and PyTorch; experience fine-tuning transformer-based LLMs at scale.
• Experience with distributed training strategies (FSDP, ZeRO, pipeline parallelism).
• Experience with RLHF, DPO, or other preference optimization techniques.
• Strong evaluation methodology, benchmarks, and human evaluation design.

Responsibilities

• Design, execute, and operationalize fine-tuning workflows for LLMs.
• Collaborate with product, design, and engineering to translate requirements into production-ready solutions.
• Lead code reviews, design reviews, and mentor junior engineers to raise engineering quality.
• Deliver shipping, production-grade LLM work with rigorous evaluation and reliability.

Skills

Python programming

ML research & engineering

Communication skills

Education

Master’s or PhD in CS/ML

Tools

PyTorch

GPU clusters

FSDP/ZeRO/PP

RLHF/DPO

Job description

Bright Vision Technologies is seeking a Generative AI Engineer for 100% remote work in the United States. You will design, implement, and operationalize fine-tuning workflows for large language models, focusing on practical production pipelines and robust evaluation.

The role requires 6+ years of ML research and engineering, strong Python and PyTorch skills, and experience with distributed training strategies and RLHF/DPO methods.