Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

Why Translucent

Healthcare providers drive $2.5 trillion in medical expenditures annually — and operate on razor-thin 2–5% margins. Despite these stakes, the finance teams behind these organizations are buried in spreadsheets, manual data pulls, and disconnected systems, spending more time finding and cleaning data than actually using it to make decisions.

Translucent is changing that. We're building the agentic AI platform designed exclusively for healthcare finance — giving every finance team, department, service line their own arsenal of AI Agents that run 24/7, understand their specific data, business logic, and workflows.

Founded in 2024 and backed by GV, NEA, FPV, and Virtue, we've already been deployed by healthcare organizations managing over $5 billion in combined revenue. The product-market fit is real, the problem is massive, and we're just getting started. If you want to work at the intersection of AI and one of the most complex, consequential industries in the world — this is the place.

About the role

We're hiring a Forward Deployed Insights Engineer to join our team in New York. You will sit at the intersection of our customers' healthcare finance problems and the agentic AI and ML systems we build to solve them. Working on top of the unified data ontology our Analytics Engineers maintain and the agent infrastructure our AI Engineering team builds, you will stand up new agents and ML models for specific customer problems, configure the visualizations and presentation layer that make their outputs usable, and work directly with customers and end users to turn those capabilities into real, trusted decisions.

This is a high-ownership, customer-facing role. You will partner closely with our product team to make sure the solutions you build for one customer become repeatable, platform-wide offerings.

What you'll do

Configure customer workspaces and build new AI agents and ML models on top of existing agent infrastructure and platform capabilities — our AI Engineering team owns agent infra, development harnesses, and core architecture, so you're focused on building for the customer problem, not the underlying framework.

Work directly with customers and our product team to scope solutions that solve real, high-value problems in healthcare finance.

Configure the visualizations and presentation layer for the data, trends, and insights your agents and models generate — turning model output into something a finance or operations leader can act on.

Present and demo ML/AI solutions directly to end users — translating technical capability into business trust.

Partner with product to generalize customer-specific solutions into reusable, platform-wide features.

Validate the correctness and reliability of the agents and models you ship, and build the testing rigor needed to earn customer trust in AI-generated outputs.

What we're looking for

Must-haves

Deep healthcare domain experience — consulting, forward-deployed, or health tech background, with hands-on exposure to claims, EHR, or financial/revenue cycle data.

Strong Python and SQL.

Real, hands-on experience using AI-assisted coding tools (e.g., Cursor, Claude Code) in your day-to-day workflow.

Exposure to AI/ML projects or programs — agentic systems, applied ML, or similar.

Comfort building visualizations or front-end presentation layers for data and analytical output (e.g., dashboards, BI tools, or lightweight front-end frameworks).

Experience working directly with end users, including presenting technical or AI/ML solutions to non-technical stakeholders.

Strong experience with version control and gitops workflows — you're comfortable owning changes through code review and shipping them via automated CI/CD, not just committing code.

Nice-to-haves

GCP experience.

Experience productizing a bespoke customer solution into a reusable, platform-wide feature.

Education

Bachelor's degree (or higher) in computer science, data science, statistics, mathematics, or a related quantitative field — or equivalent experience building and deploying applied ML/AI solutions. Advanced degrees (MS in a quantitative field) are a plus but not required; we weigh hands-on AI/ML build experience and healthcare domain exposure over formal credentials.

Location

Union Square, New York City. In-office 4 days per week.

Compensation

$180k - $230k