Role Overview

You'll build the agentic platform at the core of Translucent, improving the underlying agent capabilities every team builds on rather than configuring agents for a single customer. You'll standardize the tool surfaces new capabilities plug into, make the platform integration-ready across products, and own the evals, context, and harness engineering that make it reliable enough for healthcare finance, where accuracy is non-negotiable.

If you love sweating the details of production AI, shipping fast, and seeing your work in customers' hands quickly, you'll feel at home here.

What You'll Do

Standardize tool and skill surfaces. Define the MCP-style tool, connector, and skill contracts that let us add capabilities continuously without destabilizing the platform.
Make the platform integration-ready. Build the connective tissue so agents, tools, context, and data compose cleanly across products, and match the right architecture to each use case.
Own evals and benchmarks. Build production-grade eval harnesses, replay, and benchmarks, and gate every release on them.
Lead context and harness engineering. Ground agents in each customer's business rules and data, and build the control loops that keep outputs reliable.
Fine-tune and evaluate models. Fine-tune in-house and open-source models, benchmark against frontier baselines, and make the build-vs-buy calls.

You will sit at the intersection of customer healthcare finance problems and the AI that solves them. Analytics Engineers get the data in accurately and encode customer business logic. This role builds on top of that: the context layer agents operate on, tailored agents and predictive models for each customer, and the iteration that turns outputs into decisions and behavioral change. You will work in Python with the company's agent harnesses and eval tooling, partnered with the value team and directly with customers.

You'll feel at home here if you were called a data scientist three to five years ago and have been shipping applied AI since.

What You'll Do:

Build tailored agents. Stand up customer-specific agents and ML models on existing agent infrastructure.
Own the context layer. Maintain the customer context agents operate on, including platform-level agents.
Validate outputs. Use harnesses and evals to keep agents and models accurate enough to earn trust.
Drive decisions. Iterate on insights and presentation with the value team until customers act on them.
Work with customers. Scope problems with end users and present solutions to finance and ops leaders.

Applied AI Engineer

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