Remote AI Applications Engineer for LLM Fine-Tuning

Bright Vision Technologies; Florida; Remote; USD 100,000 - 175,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Applications Engineer to design, execute, and operationalize fine‑tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches.

The role requires deep experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and engineering discipline to operate complex training pipelines reliably.

Qualifications

• Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience.
• Six+ years of combined ML research and engineering experience with significant LLM exposure.
• Strong proficiency in Python and modern deep learning frameworks, especially PyTorch.
• Hands-on experience fine-tuning transformer-based language models at non-trivial scale.
• Familiarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelism.
• Experience with RLHF, DPO, or other preference optimization techniques.
• Strong understanding of evaluation methodology, benchmarks, and human evaluation design.
• Experience operating training jobs on GPU clusters and recovering from failures.
• Strong written and verbal communication skills.
• Track record of shipping or publishing impactful LLM work.

Responsibilities

• Translate ambiguous requirements into well-engineered AI solutions.
• Collaborate with cross-functional partners to ship production-grade ML workflows.
• Mentor junior engineers and contribute to code/design reviews.

Skills

ML Research

LLM Experience

Python & PyTorch

Fine-tuning LMs

Distributed Training

RLHF / DPO

Evaluation Methodology

GPU Training

Communication Skills

Shipping LLM Work

Education

MS/PhD in CS/ML

Tools

FSDP

ZeRO

Pipeline Parallelism

Job description

Bright Vision Technologies is seeking an AI Applications Engineer to design, execute, and operationalize fine‑tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches.

The role requires deep experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and engineering discipline to operate complex training pipelines reliably.