Senior LLM Engineer — Remote, Production-Grade Fine-Tuning

Bright Vision Technologies; Edison (NJ); Remote; USD 105,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an LLM Engineer for a 100% remote U.S. position. You will design, execute, and operationalize fine-tuning workflows for large language models with supervision, preferences, and RL methods.

Expect strong collaboration with product and engineering teams and production-grade pipelines. The role requires 10+ years of ML research/engineering, expert Python/PyTorch skills, and experience with distributed training on GPU clusters.

Qualifications

• Master’s or PhD in CS/ML or related field or equivalent experience.
• 10+ years of ML research and engineering experience with significant LLM exposure.
• Strong proficiency in Python and PyTorch; hands-on fine-tuning of transformer models.
• Experience with distributed training strategies and large-scale GPU workloads.

Responsibilities

• Design and execute fine-tuning experiments for LLMs using supervised, DPO, RLHF.
• Lead dataset construction, curation, and QA for instruction tuning and preference data.
• Build scalable training pipelines on modern distributed frameworks.
• Tune hyperparameters and training stability for large-model fine-tuning.
• Implement parameter-efficient fine-tuning methods (LoRA, QLoRA, adapters).
• Design rigorous evaluation suites including automated benchmarks and human eval.

Skills

Python

PyTorch

LLM fine-tuning

Distributed training

RLHF / DPO

Evaluation

Communication

Education

Master’s or PhD in CS/ML or related field

Tools

FSDP

ZeRO

Pipeline parallelism

Job description

Bright Vision Technologies is seeking an LLM Engineer for a 100% remote U.S. position. You will design, execute, and operationalize fine-tuning workflows for large language models with supervision, preferences, and RL methods.

Expect strong collaboration with product and engineering teams and production-grade pipelines. The role requires 10+ years of ML research/engineering, expert Python/PyTorch skills, and experience with distributed training on GPU clusters.