Large Language Model Specialist

Bright Vision Technologies; South Jordan, Draper (UT, UT); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking a Large Language Model Specialist to design, implement, and optimize fine-tuning workflows for state-of-the-art LLMs. This is a 100% remote, direct W2 full-time position within a U.S.-based team, offering substantial growth as you work cross-functionally with product, design, and engineering to ship production-grade models.

The ideal candidate has 6+ years in ML research and engineering, deep experience with PyTorch, transformer fine-tuning, and distributed

Qualifications

• Master’s or PhD in CS/ML or related field; or equivalent experience.
• 6+ years in ML research/engineering with significant LLM exposure.
• Proficiency in Python and PyTorch.
• Experience fine-tuning transformer models at scale.
• Familiarity with distributed training (FSDP, ZeRO, pipeline parallelism).
• Experience with RLHF, DPO, or other preference optimization techniques.

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 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 such as LoRA, QLoRA, and adapter-based methods.
• Design rigorous evaluation suites including automated benchmarks and human evaluation.
• Operate large-scale training jobs on GPU clusters and diagnose failures.
• Collaborate with product, research, and platform teams to align roadmaps with business needs.
• Document training methodology, results, and decisions clearly for technical and non-technical audiences.
• Mentor engineers on fine-tuning best practices and responsible deployment.

Skills

Python

PyTorch

Fine-tuning transformers

RLHF

DPO

Evaluation methodology

Distributed training

GPU clusters

Communication

LLM shipping

Education

Master’s or PhD in CS/ML or related field

Job description

Large Language Model Specialist – Remote

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Job Title: Large Language Model Specialist

Location: 100% Remote (U.S.)

Position Type: Full-time, Direct W2

Salary Range: $100,000–$150,000 Annually

Experience Required: 6+ years

Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position.

Job Summary

We are looking for anLLM Fine-Tuning Engineerto design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires deep practical experience with modern training stacks, careful dataset construction, rigorous evaluationmethodology, and the engineering discipline tooperatecomplex training pipelines reliably. The ideal candidate combines strong ML intuition with production-grade engineering practices, and is comfortable navigating the trade-offs between data quality, compute budget, evaluation rigor, and shipping velocity. In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineeredsolutions, andwill be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, anda track recordof shipping meaningful work that holds up well in production.

Key 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 asLoRA,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.
• Optimizetraining 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 trainingmethodology, results, anddecisions clearlyfor 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.

Required Qualifications

• Master’s or PhD in Computer Science, Machine Learning, or a relatedfield;or equivalent experience.
• Six or more years of combined ML research and engineering experience, with significant LLM exposure.
• Strongproficiencyin Python and modern deep learning frameworks, especiallyPyTorch.
• Hands-on experience fine-tuning transformer-based language models atnon-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 evaluationmethodology, benchmarks, and human evaluation design.
• Experience operating training jobs on GPU clusters and recovering from failures.
• Strong written and verbal communication skills.
• Track recordof shipping or publishing impactful LLM work.

Preferred Qualifications

• 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 toresponsible AI evaluation and red-teaming practices.

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.

BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.