Staff Software Engineer

Iterative Health•Cambridge, NY

13h

About The Position

Iterative Health is a healthcare technology and services company powering the acceleration of clinical research to transform patient outcomes. The Iterative Health Site Network is a premier network of 70+ clinical research sites across US and Europe accelerating the path to market for gastrointestinal (GI) and hepatology novel therapies. Our focus is on driving the success and growth of our partner sites by empowering them with tech-enabled services. By combining deep expertise in clinical trials with cutting-edge AI, we empower research teams and study sponsors to expand and expedite access to novel therapeutics for patients in need. About the Role Accelerating clinical research is one of the defining challenges in healthcare. Promising therapies exist that patients can't access because the operational infrastructure to run clinical trials efficiently doesn't exist yet. We're building it. That means designing technology systems that bring order to a fragmented landscape of clinical data sources, automating the operational work that slows trials down, and turning real-world clinical data into a foundation for predictive intelligence. We're looking for a Staff Software Engineer to own the design and implementation of the core systems that make this possible. This means building robust integrations across a complex, fragmented landscape of EHRs, CTMS platforms, and clinical data sources, each with its own data models, access patterns, and operational constraints. It also means building the data infrastructure that powers our predictive capabilities: the pipelines, feature stores, and training infrastructure that allow us to move from raw clinical data to models that meaningfully improve trial execution. This is a generalist role for someone who thinks in systems. You'll move between architecture and implementation, between integration engineering and ML infrastructure, between defining technical strategy and writing the code that proves it out. The problems are genuinely hard, largely unsolved, and what you build will matter. This is an opportunity for someone who wants to be a part of a small, fast moving engineering team at a formative stage. You'll shape what gets built, how decisions get made, and what the team becomes.

Requirements

Have 10+ years of software engineering experience, with significant time spent designing and building systems not just features
Experience with healthcare data systems (HL7, FHIR, EHR/EMR integrations) or other highly regulated data environments
Have 2+ years experience working in a high-growth startup environment
Have deep experience with system integration, particularly across unreliable or heterogeneous external data sources
Can think through and explain the full picture of a complex system while still being effective at the implementation level
Experience using AI tooling and frameworks as part of your engineering workflow
Have built data pipelines and infrastructure that support analytical or ML workloads in production
Strong across the backend stack (Python, SQL, cloud infrastructure) and can pick up new tools and systems quickly
Have built systems from early stages, making foundational decisions with incomplete information, not just extending established infrastructure
Naturally raise the quality of the engineering around you through code review, architecture guidance, and honest technical conversation

Nice To Haves

Experience building ML training infrastructure or feature platforms
Familiarity with clinical operations, systems, or life sciences data
SOC 2, HIPAA or similar compliance experience baked into engineering practice
A track record of building systems that had to work correctly under real operational constraints, scale and complexity

Responsibilities

Own the technical architecture for our core systems and build the foundational abstractions and interfaces that everything else is built on top of
Build reliable integrations with dozens of external clinical data systems (EHRs, CTMS, eSource, labs), treating this as a hard systems design problem, not just an ingestion problem
Build and evolve the infrastructure that supports model training and predictive capabilities, from ingestion through feature engineering and serving
Move fast by prototyping to drive experimentation; derisk ideas quickly to keep the team building what works
Evaluate and choose the technologies we build on: make build vs. buy decisions, select vendors, and shape our long-term infrastructure strategy
Make the key technical tradeoffs between speed and durability, between generality and shipping
Work across product, clinical operations, and data science to make sure you're solving the right problems, not just the interesting ones
Help shape the engineering culture of a small, growing team: how technical decisions get made, how problems get debated, what rigor looks like in practice