LLM Fine-Tuning Engineer

Bright Vision Technologies•Frisco, TX

1d•Remote

About The Position

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a skilled LLM Fine-Tuning Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Requirements

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.

Nice To Haves

Publications at top-tier ML venues.
Experience with multimodal model fine-tuning.
Familiarity with synthetic data generation and dataset distillation.
Open-source contributions to LLM training libraries.
Exposure to responsible AI evaluation and red-teaming practices.

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.