/roles — ROLE_415
Forward Deployed Machine Learning Engineer
Fast-growing company supplying training data for AI, linking data owners with model builders and expanding into evals
The role
- COMP
- $170K - $270K
- EQUITY
- Competitive equity
- LOCATION
- New York · San Francisco · South Bay Area · Los Angeles · Boston · Seattle · Texas · Chicago · Washington DC · Denver · Florida · Minnesota · Sacramento
- WORKPLACE
- Remote
- EXPERIENCE
- 3 - 8 years
- VISA
- None
- STACK
- Python, Git
- INDUSTRY
- AI, B2B, Data, Enterprise
The company
A well-funded AI data company running a platform that connects organizations holding valuable data with vetted AI builders, and preparing that data for model training.
- STAGE
- growth-stage
- FUNDING
- $60M+ raised
- TEAM
- ~50 people
- FOUNDED
- 2024
- BACKING
- backed by a16z
JD — the work
About the role
You would be the first ML engineer dedicated to a new evaluations business line, working directly with its general manager and researchers to set up the engineering groundwork for how customers evaluate AI models. The company runs a fast-growing, venture-backed platform supplying data to train AI and partners with data holders in sectors like healthcare and media. The role is remote within the US and mixes backend infrastructure with customer-facing engineering.
What you'll do
- Scope and create evaluation suites and benchmarks with first customers, covering many subject areas and data types
- Own the backend: orchestration, storage, pipelines for data, and the environments where evals execute
- Create isolated sandboxes for agent evaluations that involve tool use, running code, or long multi-step tasks
- Lead the engineering side of customer engagements from start to finish
- Notice recurring evaluation needs and missing infrastructure, and turn them into new product ideas
What they're looking for
- 4+ years in engineering, including direct work evaluating ML models
- Past responsibility for backend systems and infrastructure, including pipelines and runtime environments
- Ease with ambiguity and a tendency to act quickly in fast-changing settings
- Clear written communication for customers and cross-functional partners
Nice to have
- Background creating LLM benchmarks, eval suites, or human-labeled data pipelines