The job, in the employer's words
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 at growth-stage startups (strongly preferred)
- Experience with LLM application frameworks (e.g. RAG, agent orchestration, eval harnesses)
- Familiarity with clinical trial operations, disease classification, or patient identification problems
- Experience building or defining the roadmap for an AI function from early stages
How We Work
- Collaborative and low-ego team environment
- High ownership and accountability culture
- Fast-paced and highly iterative growth environment
- Open communication and continuous learning mindset
- Mission-driven organization focused on improving patient outcomes
- Comfortable navigating evolving business priorities and opportunities
Benefits That Support You
We believe great teams do their best work when they feel supported — professionally and personally.
- Hybrid work environment with in-office collaboration two days per week in either our NYC, Southlake or Boston office
- Comprehensive medical, dental, and vision coverage, with up to 80% of premiums covered by Iterative Health
- Mental health and wellness support through Spring Health
- Health HSA or FSA options, and commuter FSA contributions supported by Iterative Health
- Unlimited PTO, 12 company holidays, and a company-wide shutdown between Christmas and New Years
- 401(k) program with a company match of up to 3% (up to $3,000 annually)
- Weekly in-office lunch benefit every Tuesday
- 100% company-paid short-term and long-term disability coverage
- Annual wellness and professional development stipend to support your health and growth
- And more!
At Iterative Health, we’re actively working towards creating an environment that is representative of the diversity of patients our technology serves. We are focused on building an equitable and inclusive culture, and by extension, hiring process. If you require any accommodations to make the application process or interviewing experience more accessible to you, please contact CandidateAccommodations@iterative.health.
Benefits mentioned
- Hybrid work environment with in-office collaboration two days per week in either our NYC, Southlake or Boston office
- Comprehensive medical, dental, and vision coverage, with up to 80% of premiums covered by Iterative Health
- Mental health and wellness support through Spring Health
- Health HSA or FSA options, and commuter FSA contributions supported by Iterative Health
- Unlimited PTO, 12 company holidays, and a company-wide shutdown between Christmas and New Years
- 401(k) program with a company match of up to 3% (up to $3,000 annually)
- Weekly in-office lunch benefit every Tuesday
- 100% company-paid short-term and long-term disability coverage
- Annual wellness and professional development stipend to support your health and growth
- And more!
- New York pay range
- $170,000—$240,000 USD
Requirements mentioned
- 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
- Experience working at growth-stage startups (strongly preferred)
- Experience with LLM application frameworks (e.g. RAG, agent orchestration, eval harnesses)
- Familiarity with clinical trial operations, disease classification, or patient identification problems
- Experience building or defining the roadmap for an AI function from early stages
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