LLM Engineer

Bright Vision Technologies•Monroe Township, MI

16h•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is seeking an LLM Engineer to design, execute, and operationalize fine-tuning workflows for large language models using supervised, preference-based, and reinforcement learning approaches. The role involves dataset construction, evaluation, and reliable operation of complex training pipelines. The engineer will balance data quality, compute budget, evaluation rigor, and delivery needs while working with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into well-engineered solutions. The position also includes code and design reviews, mentoring junior engineers, and communicating work clearly to technical and non-technical audiences. This is a full-time, direct W2 position that is 100% remote within the United States. The salary range is $100,000–$150,000 annually. Candidates with 10 or more years of experience are sought. U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply; the company cannot sponsor new H-1B visa petitions for this position.

Requirements

Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent experience.
Ten 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 for instruction-tuning and preference data.
Build scalable training pipelines using 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 evaluation suites incorporating 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, diagnose failures, and reliably recover training state.
Optimize training throughput using mixed precision, sequence packing, and efficient attention implementations.
Manage model artifacts, lineage tracking, and reproducibility across concurrent experiments.
Collaborate with product, research, and platform teams to align fine-tuning roadmaps with business needs.
Document training methodology, results, and decisions for technical and non-technical audiences.
Mentor engineers on fine-tuning practices, evaluation rigor, and responsible deployment.
Stay current with LLM research and translate advances into production-ready fine-tuning recipes.