LLM Engineer

Bright Vision Technologies; Woodbridge Township (NJ); Remote; USD 100,000 - 150,000; Full time

Job summary

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

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

Qualifications

• Master’s or PhD in CS/ML or related field, or equivalent experience.
• 10+ years combined ML research and engineering with LLM exposure.
• Strong Python and PyTorch proficiency.
• Hands-on fine-tuning of transformer models at scale.
• Familiarity with distributed training (FSDP, ZeRO, pipeline parallelism).
• Experience with RLHF, DPO, or similar preference methods.
• Strong evaluation methodology and human evaluation design.
• Experience operating GPU training jobs and recovering from failures.
• Excellent written and verbal communication skills.
• Track record of shipping impactful LLM work.

Responsibilities

• Design and execute fine-tuning experiments for LLMs.
• Lead dataset construction and QA for instruction tuning.
• Build scalable training pipelines on modern frameworks.
• Tune hyperparameters and stability for large models.
• Implement LoRA/QLoRA and adapter-based methods.
• Design rigorous evaluation suites and benchmarks.
• Implement safety and policy evaluations across releases.
• Operate large-scale GPU training jobs and recover state.
• Mentor engineers and share best practices.

Skills

Python

PyTorch

LLM basics

Distributed training

RLHF

DPO

Evaluation design

Communication

Education

Master’s/PhD in CS/ML or related

Tools

FSDP

ZeRO

Pipeline parallelism

Job description

LLM Engineer - 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: LLM Engineer

Location: 100% Remote (U.S.)

Position Type: Full-time, Direct W2

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

Experience Required: 10+ 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 an LLM 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 practical experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to operate complex 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-engineered solutions, and will 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, and a track record of 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 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.

Required Qualifications

• 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.

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 to responsible AI evaluation and red‑team\-ing 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.