Remote Foundation Model Engineer: LLM Fine-Tuning & Systems

Bright Vision Technologies; Wildwood (MO); Remote; USD 200,000 - 230,000; Full time

Job summary

Bright Vision Technologies is seeking an experienced Foundation Model Engineer to design and operationalize large language model fine-tuning workflows. This fully remote role requires hands-on expertise with modern training stacks, dataset construction, and production-grade pipelines.

The ideal candidate has 10+ years of ML research/engineering, deep knowledge of PyTorch, distributed training, and RLHF/DPO, with a proven track record of shipping impactful LLM work in production.

Qualifications

• 10+ years of ML research and engineering experience with LLM exposure.
• Strong Python and PyTorch expertise.
• Hands-on fine-tuning of transformer-based language models at scale.
• Experience with distributed training (FSDP, ZeRO, pipeline parallelism).
• Experience with RLHF, DPO, or other preference optimization.
• Proven ability to design robust evaluation methods and benchmarks.
• Experience running GPU-cluster training and handling failures.
• Strong communication skills and track record of shipping LLM work.

Responsibilities

• Design, implement, and operate fine-tuning workflows for LLMs.
• Work with cross-functional teams to translate requirements into scalable solutions.
• Mentor junior engineers and participate in design/code reviews.
• Maintain production-grade training pipelines and monitor performance.

Skills

Python

PyTorch

LLM fine-tuning

Distributed training

RLHF/DPO

Evaluation methodology

GPU clusters

Communication skills

Shipping LLM work

Tools

FSDP

ZeRO

Pipeline parallelism

Job description

Bright Vision Technologies is seeking an experienced Foundation Model Engineer to design and operationalize large language model fine-tuning workflows. This fully remote role requires hands-on expertise with modern training stacks, dataset construction, and production-grade pipelines.

The ideal candidate has 10+ years of ML research/engineering, deep knowledge of PyTorch, distributed training, and RLHF/DPO, with a proven track record of shipping impactful LLM work in production.