Machine Learning Engineer @ Reducto
AI Startup
San Francisco, CAOn-siteFull-time$140k – $220k / year
About this role
Machine Learning Engineer @ Reducto
Location: San Francisco Office
Type: FullTime
Compensation: $150K – $300K • Offers Equity
About Reducto
Reducto is the agentic document platform for leading AI teams who demand enterprise performance at scale. We provide a comprehensive toolkit for working with documents the way a human would, combining custom in-house and leading frontier models to power efficient and accurate document workflows.
We’ve grown rapidly, increasing revenue 8x year over year and partnering with hundreds of companies, from leading AI teams like Harvey, Vanta, and Scale, to enterprise customers across FAANG and top trading firms.
Reducto has raised over $100M from world-class investors including a16z, Benchmark, and First Round Capital.
We would love to meet you if you:
- Philosophy: You are your own worst critic. You have a high bar for quality and don’t rest until the job is done right—no settling for 90%. We want someone who ships fast, with high agency, and who doesn't just voice problems but actively jumps in to fix them.
- Experience: You have 2+ years of experience with training, fine tuning, and evaluating ML models used in production systems
- Language/Skills: You’re exceptional at Python or similar, and are well versed with both traditional computer vision and VLMs
- Tools: Build your own tools as needed—like a quick Streamlit app to test hypotheses or create a dataset.
- Approach: A quantitative approach to building products. Ability to debug, experiment, and iterate fast. You should be comfortable getting hands-on with the full development lifecycle, from ideation to shipping to users.
The core work will include:
- Training and deploying new state of the art models for parsing and interpreting unstructured data
- Experimenting with novel techniques to improve LLM accuracy
- Build data pipelines, evaluate model performance, and integrate models into the product
- Working directly