/roles — ROLE_336
Member of Technical Staff
Venture-backed AI startup in New York automating finance workflows for enterprise companies
The role
- COMP
- $150K - $250K
- EQUITY
- Competitive equity
- LOCATION
- New York
- WORKPLACE
- On-site
- EXPERIENCE
- 3 - 8 years
- VISA
- None, Visa transfers
- STACK
- Python, TypeScript, NodeJS, Express, Langchain, PostgreSQL, Redis, AWS, OpenAI, REST API, Snowflake
- INDUSTRY
- AI, Finance, Software Development, Enterprise, B2B, Fintech
The company
Fast-growing startup building AI agents that plug into enterprise data and take routine work off finance teams, used by high-growth startups and Fortune 500 companies.
- STAGE
- Series A-stage
- FUNDING
- $10M+ raised
- TEAM
- ~10 people
- FOUNDED
- 2023
- BACKING
- VC-backed
JD — the work
About the role
You would build the AI agents behind finance workflows used by hundreds of enterprise teams, owning problems from user discovery through production. The team is about a dozen people, flat and in person in New York, in close contact with the founders and the engineering co-lead. Engineers here run their own product areas, ship meaningful work every week, and deal with very little bureaucracy. The company launched recently and has grown fast, with customers ranging from high-growth startups to Fortune 500 companies.
What you'll do
- Develop agentic workflows for enterprise finance using Python, LangGraph, and LangChain
- Own backend work across database schemas, APIs, and storage on Postgres, Redis, and DynamoDB
- Run your own product areas, talking with customers, designers, and the go-to-market team to decide what to build
- Create evaluation datasets, traces, and monitoring that show how agents perform
- Connect to ERPs, banking, and billing platforms, and use Temporal to coordinate the AI and backend layers
What they're looking for
- 3 to 5 years of experience at startups, or 5 to 8 years at larger tech companies
- Strong Python and TypeScript, with Node, Express, PostgreSQL, and Redis
- Experience building LLM applications or agents, ideally with LangChain or LangGraph
- Product ownership instincts and comfort talking directly with customers