Applied AI Engineer

Iterative Health is a healthcare technology and services company powering the acceleration of clinical research to transform patient outcomes.

We built a leading performance-driven network of 100+ sites across the US, Europe, India, and Australia, conducting research directly in the communities where care is delivered across gastrointestinal, hepatology, obesity, and cardiology. By combining deep clinical trial expertise with cutting-edge AI, we connect sponsors' scientific ambitions with high-performing research teams that expedite and expand access to novel therapeutics for patients in need. Today, Iterative Health is headquartered in Cambridge, Massachusetts, and New York City with 250+ employees world-wide.

We're looking for an Applied AI Engineer who can understand our data deeply enough to know what's possible, and turn that into working systems. We have a unique dataset within the industry — connecting clinical data (EMR, endoscopic video, etc.) to trial data across 80+ trial sites. The right person will dig into this data to understand its structure and gaps, build systems that translate that understanding into real outcomes for sites and patients, and bring a point of view on how new capabilities apply to our specific problems as the landscape evolves.

You'll work hands-on with the data, build and ship models, LLM-based systems, and agents,  and directly influence what we build and how. This role sits at the intersection of applied ML, engineering, and data science— you'll build the foundation for our predictive capabilities and help shape what that function becomes, on a small, fast-moving engineering team at a formative stage.

Where You’ll Drive Impact 

Work with clinical, video, and clinical trial operational data to understand what's there, what's meaningful, and how to use it to drive a more efficient clinical trial system

Build and ship ML models, LLM-powered systems, and agents — from prototype to production

Stay close to the evolving AI and ML landscape and bring a point of view on how new capabilities apply to our data and problems

Define the path from raw data to product and operationalization: what to model, how to evaluate it, and when it's ready to ship

Partner with product and engineering to translate findings into shipped features

Identify opportunities where our data creates unique predictive advantages

Evaluate where we should build, where we should partner, and where existing approaches fall short

Help shape the engineering culture of a small, growing team: how technical decisions get made, how problems get debated, and what rigor looks like in practice

Performs related duties as requested 

What You Bring to the Team 

5+ years of experience in applied AI/ML, software/ML engineering, or data science, with significant hands-on time building and shipping systems on real data

Experience with healthcare data, clinical research, or life sciences

Deep curiosity to understand data and connect it to the real world

Strong statistical foundations and the judgment to know when a result is meaningful versus interesting

Fluent in Python and SQL, comfortable across the data stack from exploratory analysis through production-ready code and pipelines

Experience working across data science, product, and engineering — able to build what's needed, not just analyze what exists

Experience building and shipping production systems — including LLM-powered features (prompting, evaluation, RAG/agent pipelines) — not just research code or notebooks

Experience with complex, messy, real-world data and how to make it useful without pretending it's clean

Knowledgeable about ML capabilities and frameworks, with the judgment to know when something is genuinely applicable versus hype

Strong communication skills — able to convey findings clearly to technical and non-technical audiences and make a persuasive case for what to build next

Preferred Qualifications

Experience working a …