Forward Deployed Insights Engineer (Applied AI). New York City to $230,000 plus bonus and equity.
Staffworx is working with a venture-backed healthcare finance AI company building the AI layer that lets health systems manage complex financial data and workflows. Headquartered in central Manhattan and founded in 2024 the business has raised $35m to date from top-tier US venture investors, is already live at organisations managing over $5bn in combined revenue. Around 27 people, real traction, early equity.https://www.staffworx.co.uk/job/forward-deployed…ngineer-new-york/
US healthcare providers account for roughly $2.5 trillion of medical expenditure on margins of two to five per cent. Finance teams inside health systems are stuck pulling numbers out of fragmented systems and rebuilding the same spreadsheets, so decisions are made on last month’s data rather than this month’s. Our client automates that work. This is deliberately not clinical workflow AI, it is agentic AI applied to healthcare finance specifically.
This is an applied data science role, not a retitled platform engineering seat. You would sit at the intersection of customer healthcare finance problems and the AI and agents that solve them, building on top of a data layer that a separate analytics engineering function maintains.
What you will be doing
- Building and configuring tailored agents and predictive models for specific customers, on existing agent infrastructure
- Owning and maintaining the customer context layer that agents operate on
- Validating outputs using harnesses and evals, so agents are trustworthy enough to act on
- Working with the value team to iterate on insights until customers actually use them to make decisions
- Working directly with customers, scoping problems with end users and presenting to finance and operations leaders
The split is roughly 80% hands-on-keyboard, 20% client-facing. Python and SQL heavy, working with the company’s own agent harness and evaluation tooling.
What we need to see
- Three to ten years in data science, applied ML, or analytics
- A track record of shipping applied ML, predictive models or AI agents to production, owned end to end
- Hands-on healthcare experience, in data, workflows, or products. Candidates with no healthcare finance exposure, or a purely clinical background, are not a fit for this specific role
- Hands-on LLM and agentic tooling, building and evaluating agents in real work rather than experimenting
- Strong Python and SQL, both genuinely hands-on
- A bachelor’s degree in a quantitative field, or equivalent applied ML work. A Georgia Tech analytics or CS master’s is a strong signal for this client specifically
- Customer-facing technical experience, forward deployed, implementation, or similar, is a plus
- Pipelining, distributed compute, or cloud experience (Snowflake, Databricks, Spark) and CI/CD or git workflow ownership are nice-to-haves rather than requirements
Working pattern Fully on-site in the New York office four days a week, with one day remote. Reports to the Head of Customer Engineering. The team is hiring three to four people into this role.
Selection process Introductory call covering role fit and interest, a technical screen on data transformation and pipeline knowledge, an on-site case study and culture round of two to three hours, and a short conversation with the founders, followed by offer.
Sponsorship: US citizens and Green Card holders only
