LLM Fine-Tuning Engineer at Bright Vision Technologies

Silver Springs, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

12 Sep, 26

Salary

150000.0

Posted On

14 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

LLM Fine-Tuning, PyTorch, Python, RLHF, DPO, LoRA, QLoRA, Distributed Training, FSDP, ZeRO, Dataset Curation, GPU Cluster Operation, Model Evaluation, Transformer Models, Mixed Precision, Sequence Packing

Industry

Information Technology & Services

Description

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. LLM Fine-Tuning Engineer Job Title: LLM Fine-Tuning Engineer Salary Range: 100k$/Annum-150k$/Annum Location: 100% Remote (Continental United States) Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor) Experience: 6+ years Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for qualified candidates. Employment Type: Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party) Engagement: Long-term, multi-year, aligned to the Bright Vision SOW delivery roadmap Compensation: Competitive base salary commensurate with experience, plus benefits. Employment Terms & Visa Policy This is a 100% remote, full-time, direct W2 position with Bright Vision Technologies. This role is part of Bright Vision Technologies’ in-house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies — there is no third-party client, vendor, or implementation partner involved. We do not engage in C2C, 1099, or third-party arrangements for this role. BUT STRICTLY NO C2C/1099/3RD PARTY COMPANIES. ALL OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE. Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies and contribute to our in-house SOW deliverables. No new H1B sponsorship is available for this role. However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates. For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands-on experience. Job Summary We are looking for an LLM Fine-Tuning 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. 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 boon@bvteck.com or contact us at (908) 650-6699. Learn more about Bright Vision Technologies at www.bvteck.com. We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Bright Vision Technologies is an Equal Opportunity Employer, including Disability/Veterans. Position offered by “No Fee Agency.” 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.

Responsibilities

Design and execute fine-tuning workflows for large language models using supervised, preference-based, and reinforcement learning techniques. Build scalable training pipelines and rigorous evaluation suites to ensure model performance and safety in production.