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 Data Scientist to be the person who understands our data deeply enough to know what's possible and curious enough to prove it. We have a truly unique data set within the industry, connecting clinical data (emr, endoscopic video, etc…) to trial data across 80+ trial sites. We're looking for someone who wants to dig deeply into this data - to understand its structure, its gaps, what it can tell us - and connect that understanding to real outcomes for sites and patients. The landscape is evolving rapidly, and the right person will have a point of view on how to apply new capabilities to our specific data and problems as they emerge.You'll work hands-on with the data, structure experiments, evaluate what's modelable, and directly influence what we build and how. This role sits at the intersection of data science, product strategy, and ML: you'll lay the foundation for our predictive capabilities and shape what that function becomes.

This is an opportunity for someone who wants to be 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.

Responsibilities

• Work with clinical, video, and clinical trial operational data to understand what's there, what's meaningful, and how we can use it to drive a more efficient clinical trial system
• Design and run experiments that determine what's worth building
• Stay close to the evolving ML and model 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 concrete product decisions
• 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, what rigor looks like in practice

What We’re Looking For

Required Qualifications

• 5+ years of experience in data science, applied ML, or quantitative research, with significant time spent hands-on with data
• Experience with healthcare data, clinical research, or life sciences
• A deep curiosity to understand data and connect it to the real world
• Deep experience designing and running experiments: you know how to structure a question, test it honestly, and draw conclusions that hold up
• Strong statistical foundations and the judgment to know when a result is meaningful versus interesting
• Fluent in Python and SQL, comfortable working across the data stack from exploratory analysis through to production-ready pipelines
• Experience working across data science, product, and engineering: you can influence what gets built, not just analyze what exists
• Have worked with complex, messy, real-world data and know 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
• Can communicate 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 medical imaging or video data
• Familiarity with clinical trial operations, disease classification, or patient identification problems
• Experience building or defining the roadmap for an ML function from early stages
  • A track record of data science work that directly changed product direction

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 building a uniquely valuable data asset: real-world patient and research data flowing across 80+ trial sites, spanning dozens of EHRs and clinical systems, focused on patient populations that are chronically underserved by existing clinical research infrastructure. Your job is to build the pipelines, data models, and AI infrastructure that make this asset real, from ingestion and normalization through to the systems that power predictions on top of it. You'll own data quality and observability as foundational engineering problems. You'll also have a direct hand in shaping how this data drives our AI strategy, what we model, what we predict, and what becomes possible.

This is an opportunity for someone who wants to be 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.

Responsibilities

• Own the data layer and architecture:  the models, schemas, and infrastructure decisions that everything downstream depends on
• Build and operate the pipelines and transformations that move data from ingestion through normalization, enrichment, and into the formats that support analytics, ML training, and production model serving
• Own data quality and observability: build the systems that make data issues visible and correctable before they compound
• Partner with ML and engineering teams to identify what's modelable, define training data requirements, and build the data foundations for new predictive capabilities
• Define how clinical and operational data is governed across the system
• Evaluate and select the tools and technologies that make up the data stack, with a clear point of view on build vs. buy
• 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

What We’re Looking For

Required Qualifications

• 10+ years of experience in data engineering or related roles, with significant time spent building data systems
• Experience with healthcare data strongly preferred (HL7, FHIR, claims, EHR extracts) or other complex, regulated data domains
• Deep experience modeling and integrating data from multiple heterogeneous sources with inconsistent schemas and quality
• Experience applying AI and LLMs to data engineering problems: extraction, normalization, classification, entity resolution
• Strong understanding of how data infrastructure supports ML workflows from feature engineering to training data pipelines to model serving
• Fluent in SQL and at least one modern programming language (Python, Java, Scala, Go), with experience across modern data infrastructure - distributed processing, streaming, cloud-native storage, orchestration, and transformation frameworks
• Have built data systems from early stages, making foundational decisions with incomplete information
• Naturally raise the quality of the engineering around you through code review, design guidance, and honest technical conversation

Preferred Qualifications

• Experience building data infrastructure that directly supports ML model training and evaluation
• Familiarity with clinical trial operations, EDC systems, or life sciences data
• SOC 2, HIPAA or similar compliance experience baked into engineering practice
• A track record of building or improving data systems that others had given up on making reliable

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.

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

What We’re Looking For

Required Qualifications

• 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

Preferred Qualifications

• 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