Applied AI Engineer - Hybrid Healthcare ML Systems

TAU Ventures; Cambridge (MA); Hybrid; USD 170,000 - 240,000; Full time

Benefits offered by this job

Hybrid work

Medical coverage

Wellness programs

401(k) match

Job summary

Iterative Health, headquartered in Cambridge, MA, is seeking an Applied AI Engineer to turn complex clinical datasets into practical, high-impact systems. You will work at the intersection of applied ML, data engineering, and product, building models, LLM-based capabilities, and decisioning agents within a small, fast-moving team at a formative stage.

You will translate raw clinical and operational data into deployable features, collaborate with product and engineering, and shape the future of

Qualifications

• 5+ years of experience in applied AI/ML or data science.
• Fluent in Python and SQL, end-to-end data pipelines.
• Experience with healthcare data or clinical research.
• Ability to ship ML models, LLM-based systems, and agents.

Responsibilities

• Work with clinical, video, and trial data to drive a more efficient system.
• Build and ship ML models, LLM-powered systems, and agents from prototype to production.
• Stay close to AI/ML landscape and apply insights to data and problems.
• Define data-to-product path: what to model, how to evaluate, when to ship.
• Partner with product and engineering to translate findings into shipped features.
• Identify opportunities where data provides predictive advantages.
• Help shape a small, growing engineering culture with rigorous decision-making.

Skills

Python

SQL

Applied AI/ML

Healthcare data

Production systems

Job description

Iterative Health, headquartered in Cambridge, MA, is seeking an Applied AI Engineer to turn complex clinical datasets into practical, high-impact systems. You will work at the intersection of applied ML, data engineering, and product, building models, LLM-based capabilities, and decisioning agents within a small, fast-moving team at a formative stage.

You will translate raw clinical and operational data into deployable features, collaborate with product and engineering, and shape the future of