Remote Generative AI Engineer: LLMs & Production

Bright Vision Technologies; Palo Alto (CA); On-site; USD 90,000 - 115,000; Full time

Job summary

Bright Vision Technologies is seeking a Generative AI Engineer to design and operationalize fine-tuning workflows for large language models. You will work with cross-functional partners to translate ambiguous requirements into well-engineered solutions and contribute to production-grade training pipelines.

The role emphasizes strong ML intuition, rigorous evaluation, and the ability to balance data quality, compute budget, and shipping velocity while mentoring more junior engineers.

Qualifications

• Master’s or PhD in CS/ML, or equivalent experience.
• 6+ years of ML research and engineering with LLM exposure.
• Proficiency in Python and PyTorch.
• Experience fine-tuning transformer models at scale.
• Distributed training knowledge (FSDP/ZeRO/pipeline).
• Experience with RLHF or other preference optimization techniques.
• Strong evaluation design and benchmarks; human evaluation.
• Experience running GPU-based training jobs and fault recovery.
• Excellent written and verbal communication; track record of shipping LLM work.

Responsibilities

• Design, implement, and optimize fine-tuning workflows for large language models.
• Collaborate with product, design, and engineering teams to translate requirements into ML solutions.
• Manage and monitor training pipelines; ensure reliability and scalability.
• Evaluate models using robust benchmarks and human feedback.
• Mentor junior engineers and contribute to code reviews and design reviews.

Skills

Python & PyTorch

LLM Fine-tuning

Distributed Training

RLHF / Preference Optimization

Evaluation Methodology

GPU Training Experience

Communication Skills

Shipping LLM Work

Education

Master’s or PhD in CS/ML

Tools

PyTorch

Job description

Bright Vision Technologies is seeking a Generative AI Engineer to design and operationalize fine-tuning workflows for large language models. You will work with cross-functional partners to translate ambiguous requirements into well-engineered solutions and contribute to production-grade training pipelines.

The role emphasizes strong ML intuition, rigorous evaluation, and the ability to balance data quality, compute budget, and shipping velocity while mentoring more junior engineers.