The role
- COMP
- $200K - $300K
- EQUITY
- Competitive equity
- LOCATION
- San Francisco
- WORKPLACE
- On-site
- EXPERIENCE
- 3+ years
- VISA
- None, Visa transfers
- STACK
- Python, Kubernetes, PyTorch, Docker
- INDUSTRY
- AI, Data, Enterprise
/roles — ROLE_312
ML Infrastructure Engineer
Fast-growing Series B company whose AI turns messy enterprise documents into data LLMs can use
The company
Agentic document platform used by leading AI teams to parse and process complex documents with enterprise performance at scale.
- STAGE
- scale-up
- FUNDING
- $100M+ raised
- TEAM
- ~80 people
- FOUNDED
- 2023
- BACKING
- backed by a16z, Y Combinator, Benchmark
JD — the work
About the role
You would run model training and inference infrastructure at a quickly growing company that uses AI to process complex documents. The ideal person is a broad generalist who knows how models behave in production, including monitoring, serving and data pipelines, and who can make inference faster, more reliable and cheaper. It is a high-impact individual contributor role, partnering with the ML researchers so models reach customers quickly and infrastructure never holds the product back.
What you'll do
- Own the serving stack so inference stays fast, observable and dependable for customers
- Improve training setups for models from hundreds of millions to tens of billions of parameters on a few nodes
- Add logging, metrics and alerting to every layer of the machine learning platform
- Create data pipelines and internal tools that shorten the path from a research experiment to a shipped model
- Design a layer that routes inference among several cloud providers, trading off cost, latency and accuracy
What they're looking for
- 3+ years of experience in ML infrastructure or a closely related role
- Broad understanding of how models behave in production, from serving through data pipelines
- Hands-on experience with Python, PyTorch, Docker and Kubernetes
- AI-native habits and comfort with small-cluster training and single- or dual-node serving
- Comfort working on-site full time at a fast-moving startup