Remote LLM Engineer - Fine-Tuning & Pipelines

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

Job summary

Bright Vision Technologies is seeking an LLM Engineer to design, execute, and operate fine-tuning workflows for large language models across supervised, RLHF, and related approaches. You will build scalable pipelines, curate datasets, and advance evaluation rigor with production-grade practices.

The role demands deep ML intuition, strong engineering discipline, and the ability to ship reliable, auditable results while collaborating with product, design, and operations teams.

Qualifications

• Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience.
• Six or more 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

• Design and execute fine-tuning experiments for large language models using supervised, DPO, RLHF, and related techniques.
• Lead dataset construction, curation, and quality assurance processes for instruction tuning and preference data.
• Build scalable training pipelines on top of modern distributed training frameworks.
• Tune hyperparameters, optimizer configurations, and training stability strategies for large-model fine-tuning.
• Implement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and adapter-based methods.
• Design rigorous evaluation suites including automated benchmarks, human evaluation, and capability-specific probes.
• Implement safety, refusal, and policy evaluations to track model behavior across releases.
• Operate large-scale training jobs on GPU clusters, diagnosing failures and recovering training state reliably.
• Optimize training throughput using mixed precision, sequence packing, and efficient attention implementations.
• Manage model artifacts, lineage tracking, and reproducibility across many concurrent experiments.
• Collaborate with product, research, and platform teams to align fine-tuning roadmaps with business needs.
• Document training methodology, results, and decisions clearly for technical and non-technical audiences.
• Mentor engineers on fine-tuning best practices, evaluation rigor, and responsible deployment.
• Stay current with LLM research and translate advances into production-ready fine-tuning recipes.

Skills

Python programming

PyTorch

Strong communication

GPU clusters

Education

Master’s or PhD in Computer Science/ML or related field

Tools

FSDP

ZeRO

pipeline parallelism

RLHF

DPO

Job description

Bright Vision Technologies is seeking an LLM Engineer to design, execute, and operate fine-tuning workflows for large language models across supervised, RLHF, and related approaches. You will build scalable pipelines, curate datasets, and advance evaluation rigor with production-grade practices.

The role demands deep ML intuition, strong engineering discipline, and the ability to ship reliable, auditable results while collaborating with product, design, and operations teams.