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LLM Fine-tuning Engineer
Noah Sinclair
Full-time · Mid-level · New_York
About the role
We've been building for 18 months. We have paying customers. We have a product that works — mostly because we ship fast and fix faster. The AI core is a fine-tuned language model for legal tech: contract clause identification, obligation extraction, and risk flagging in vendor agreements. The base model is good. The fine-tuning is good enough. 'Good enough' is where we are now, and we need to reach 'excellent' before a better-funded competitor does. That's the job: take our existing fine-tuning process — a script one of our co-founders wrote at 2am and has been incrementally improved ever since — and make it excellent. Better data curation, better training runs, better evaluation, faster iteration. The stack is Python, Hugging Face Transformers, PyTorch, and AWS SageMaker for training jobs. We're a team of eight. Everyone ships code. You'll work directly with the CTO and two ML engineers, and your results will directly shape the product roadmap.
Responsibilities
- Redesign and implement the data curation and cleaning pipeline for fine-tuning datasets
- Run systematic training experiments comparing fine-tuning strategies and document results clearly
- Build an evaluation framework for domain-specific output quality beyond perplexity — task-specific metrics, human eval design
- Reduce fine-tuning iteration cycle time from 4–5 days to under 2 days
- Collaborate with the product team to identify capability gaps and translate them into training objectives and data requirements
Requirements
- 3–5 years of ML engineering with 2+ years of hands-on LLM fine-tuning in a production context
- Hugging Face — Transformers, PEFT/LoRA, Datasets — production experience, not just tutorial familiarity
- PyTorch for custom training logic, debugging, and custom loss functions
- AWS SageMaker for managing training jobs and experiment tracking
- Strong data curation instincts — you know fine-tuning quality is bottlenecked by dataset quality and have opinions about fixing it
- Python for data processing pipelines, training scripts, and evaluation automation
- Experience with instruction tuning, RLHF, or DPO is a meaningful advantage
Benefits
- Direct ownership of the AI core of a product used daily by paying customers
- Full remote, flexible hours
- $100,000 – $128,000 base salary + meaningful equity in a pre-Series A company with strong growth
- Small team — you'll have context across the full product, not just your layer
- Fast iteration: you propose an experiment on Monday, run it by Thursday, and review results by Friday
Job Type
Full-time
Level
Mid-level
Language
English
Salary Range
$100,000 – $128,000
AI Expertise
AI & Machine Learning Engineers
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