Remote LLM Fine-Tuning Engineer

Bright Vision Technologies; Renton (WA); On-site; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking a Large Language Model Specialist to design and operate fine-tuning pipelines for LLMs in a remote U.S. setting. You will build scalable training workflows, optimize performance, and deliver production-ready results.

You will collaborate with product, design, and platform teams to translate requirements into robust solutions, mentor engineers, and stay current with LLM research to push capabilities forward.

Qualifications

• Master’s or PhD in Computer Science, ML, or related field, or equivalent experience.
• 6+ years of ML research/engineering with significant LLM exposure.
• Strong Python proficiency and familiarity with modern DL frameworks (PyTorch).
• Hands-on experience fine-tuning transformer-based models at scale.
• Experience with distributed training strategies (FSDP, ZeRO, pipeline parallelism).
• Experience with RLHF, DPO, or other preference optimization techniques.

Responsibilities

• Design and execute fine-tuning experiments for LLMs using supervised, RLHF, and related techniques.
• Lead dataset construction and quality assurance 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 evaluation.
• Operate large-scale GPU training jobs and recover training state.
• Collaborate with product, research, and platform teams to align roadmaps.

Skills

Python

LLM fine-tuning

Distributed training

RLHF/DPO

Communication

Education

Master’s or PhD in Computer Science / ML

Tools

PyTorch

FSDP

ZeRO

Pipeline parallelism

Job description

Bright Vision Technologies is seeking a Large Language Model Specialist to design and operate fine-tuning pipelines for LLMs in a remote U.S. setting. You will build scalable training workflows, optimize performance, and deliver production-ready results.

You will collaborate with product, design, and platform teams to translate requirements into robust solutions, mentor engineers, and stay current with LLM research to push capabilities forward.