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

Location: 100% Remote (Continental United States)

Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor)

Experience: 6+ years

Salary: 100K - 150K

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

1. Design and execute fine-tuning experiments for large language models using supervised, DPO, RLHF, and related techniques
2. Lead dataset construction, curation, and quality assurance processes for instruction tuning and preference data
3. Build scalable training pipelines on top of modern distributed training frameworks
4. Tune hyperparameters, optimizer configurations, and training stability strategies for large-model fine-tuning
5. Implement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and adapter-based methods
6. Design rigorous evaluation suites including automated benchmarks, human evaluation, and capability-specific probes
7. Implement safety, refusal, and policy evaluations to track model behavior across releases
8. Operate large-scale training jobs on GPU clusters, diagnosing failures and recovering training state reliably
9. Optimize training throughput using mixed precision, sequence packing, and efficient attention implementations
10. Manage model artifacts, lineage tracking, and reproducibility across many concurrent experiments
11. Collaborate with product, research, and platform teams to align fine-tuning roadmaps with business needs
12. Document training methodology, results, and decisions clearly for technical and non-technical audiences
13. Mentor engineers on fine-tuning best practices, evaluation rigor, and responsible deployment
14. Stay current with LLM research and translate advances into production-ready fine-tuning recipes
15. 
    

Required Qualifications

1. Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience
2. Six or more years of combined ML research and engineering experience, with significant LLM exposure
3. Strong proficiency in Python and modern deep learning frameworks, especially PyTorch
4. Hands-on experience fine-tuning transformer-based language models at non-trivial scale
5. Familiarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelism
6. Experience with RLHF, DPO, or other preference optimization techniques
7. Strong understanding of evaluation methodology, benchmarks, and human evaluation design
8. Experience operating training jobs on GPU clusters and recovering from failures
9. Strong written and verbal communication skills
10. Track record of shipping or publishing impactful LLM work
11. 
    

Preferred Qualifications

1. Publications at top-tier ML venues
2. Experience with multimodal model fine-tuning
3. Familiarity with synthetic data generation and dataset distillation
4. Open-source contributions to LLM training libraries
5. Exposure to responsible AI evaluation and red-teaming practices

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