LLM Engineer (AI)

Location: 100% remote within the United States

Salary: $72,000–$100,000 annually

Company

Technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States.

What you will do

• Design, execute, and operationalize fine-tuning workflows for large language models using supervised, preference-based, and reinforcement learning approaches.
• Construct high-quality datasets and develop rigorous benchmark and human evaluation methodologies.
• Operate training jobs on GPU clusters, including failure recovery and reliability improvements.
• Navigate trade-offs between data quality, compute budgets, evaluation rigor, and shipping velocity.
• Collaborate with product, design, engineering, operations, and business stakeholders to turn ambiguous requirements into engineered solutions.
• Contribute through code reviews, design reviews, and mentorship of junior engineers.

Requirements

• Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent experience.
• Six or more years of combined machine learning research and engineering experience with significant LLM exposure; the position lists 8+ years of experience.
• Strong proficiency in Python and modern deep learning frameworks, especially PyTorch.
• Hands-on experience fine-tuning transformer-based language models at non-trivial scale.
• Experience with FSDP, ZeRO, pipeline parallelism, RLHF, DPO, or other preference optimization techniques.
• Applicants must be U.S. citizens, Green Card holders, EAD holders, or H-1B transfer candidates; new H-1B visa petitions cannot be sponsored.

Nice to have

• Publications at top-tier machine learning venues.
• Experience with multimodal model fine-tuning.
• Experience with synthetic data generation, dataset distillation, or open-source LLM training libraries.
• Exposure to responsible AI evaluation and red-teaming practices.

Culture & Benefits

• Full-time direct W-2 employment.
• Remote work within the United States.
• Cross-functional collaboration with product, design, engineering, operations, and business stakeholders.
• Opportunities for career growth, technical mentorship, and meaningful production work.