LLM Engineer

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: $72,000–$100,000 Annually
Experience Required: 8+ 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.

Required 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.

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-teaming practices.

How to Apply
Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected]. Learn more about Bright Vision Technologies at www.bvteck.com.
Bright Vision Technologies is an Equal Opportunity Employer.
 

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.