Translucent AI is building an agentic platform tailored for healthcare finance, enabling finance teams to deploy AI-powered agents that operate across systems with reliability and scale. In this hybrid New York role, you will shape the core platform by standardizing tool interfaces, enabling clean integrations, and driving production-grade reliability across product lines.
Role overview
As an AI Engineer, you will contribute across research, development, and platform engineering to elevate the shared agent capabilities used by multiple products. You will standardize tool surfaces and contracts to scale without destabilizing the platform, prepare the agent platform for integration across offerings, and own the evaluation, context, and harness engineering that underpins dependable performance in healthcare finance. You will determine the appropriate agentic architecture for each use case and help turn core capabilities into a reusable ecosystem that spans features and teams.
Responsibilities
- Standardize tool surfaces and contracts, including MCP-style tool, connector, and skill interfaces, to enable new capabilities without destabilizing the platform as it grows
- Make the agent platform integration-ready by building the connective tissue so agents, tools, context, and data compose cleanly across products and match the right architecture to each use case
- Own evaluations and benchmarks by developing production-grade eval harnesses, replay systems, and benchmarks for agentic AI, gating releases on results with accuracy critical to healthcare finance
- Lead context and harness engineering to ground agents in customer business rules and data, and implement reliable control loops that preserve output quality
- Fine-tune and evaluate models, including in-house and open-source options, benchmark against frontier baselines, and make build-vs-buy decisions on models, frameworks, and infrastructure
- Transform capabilities into an ecosystem by creating reusable core components that translate across features and products and ship end-to-end with product and design
Requirements
- 3+ years of software engineering experience with meaningful production work in Python
- Direct experience shipping LLM-powered or agentic systems in production, not just prototypes, with a clear understanding of failure modes and reliability
- Hands-on experience with at least one modern agent framework (LangGraph, Google ADK, LlamaIndex, Claude Agent SDK, or equivalent) and a clear stance on when to use vs. build
- Production-grade evals and benchmarking experience that gates real releases
- Context engineering experience, grounding agents through retrieval, context layers, and knowledge transfer to align outputs with a specific organization
- Comfort with data and retrieval layers, including SQL, BigQuery or comparable warehouses, embeddings, and vector search
- A bias toward shipping, taking ambiguous problems to a working V1 quickly and iterating thereafter
Technologies
- Python
- LangGraph
- Google ADK
- LlamaIndex
- Claude Agent SDK
- SQL
- BigQuery
- Vertex AI
- Gemini
- Claude on Vertex
- GenKit
- MCP
Role details
- Full-Time
- Office Location: New York City, NY (hybrid)
Why Translucent
Healthcare providers drive trillions in expenditures each year and operate on slim margins, yet finance teams remain bogged down by spreadsheets and fragmented data. Translucent is changing that by building an agentic AI platform tailored for healthcare finance, giving each finance function its own set of AI Agents that run continuously, understand specific data, business logic, and workflows. Founded in 2024 and backed by GV, NEA, FPV, and Virtue, the company has already been deployed by healthcare organizations with billions in revenue under management, and the journey is only beginning. This is a chance to work at the crossroads of AI and one of the most impactful industries in the world.
Expertise areas
- Tool-surface and connector platform engineering with standardized contracts and scalable ecosystems
- Harness and loop engineering for reliable agent control and per-use-case architecture sizing
- Open-source or in-house model fine-tuning and benchmarking against strong baselines
- Production context-layer engineering to reliably ground agents in business rules and preferences
Nice to have
- Experience with healthcare finance, accounting, or structured financial data
- Applied machine learning or model-evaluation research background
- Open-source contributions to agent or LLM tooling
- Experience designing tool-use APIs or developer-facing AI products
- Familiarity with Vertex AI, Gemini, Claude on Vertex, GenKit, MCP, and BigQuery
Experience level
We seek engineers who own meaningful surface areas, make informed architecture decisions, and drive work forward independently with a bias toward delivering a usable V1 quickly and a plan to iterate for reliability in healthcare finance.
Compensation
Base Salary: $175,000-$275,000 USD + equity
