Foundation Model Engineer

Role Description

This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

We are looking for a Foundation Model 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
• Engineering discipline to operate complex training pipelines reliably

The ideal candidate combines strong ML intuition with production-grade engineering practices and is comfortable navigating trade-offs between:

• Data quality
• Compute budget
• Evaluation rigor
• 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. You will be expected to raise the bar through:

• Code review
• Design review
• 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.

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

Requirements

• 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

Benefits

• 100% Remote (U.S.)
• Full-time, Direct W2
• Salary Range: $100,000–$150,000 Annually
• Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply

How to Apply

Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at View phone number on remotive.com.