Careers at Pelica Health
Build the operating system for value-based care.
Pelica Health is a Y Combinator backed AI company. We unify claims, EHR, pharmacy, lab, and ADT data into one live record per member, then put an AI copilot next to every team that depends on it: risk adjustment, Quality and Stars, pharmacy and Part D, provider network, and care management.
Founded by former engineering and AI leaders from Google and YouTube. Catherine built first-of-kind enterprise AI agents for procurement and finance teams at Google and shipped the YouTube Shopping launch as a software engineer. Lalit was a Staff Engineer and tech lead in YouTube Commerce Billing for a 45-engineer org, and is a Forbes Technology Council member and ACM ICPC World Finalist. We have raised $2.5M.
We hire people who want to ship, learn fast, and own outcomes. We are five people today. The work is technical, the customers are real, and the problems matter.
Open roles
Full-Stack Software Engineer
San Francisco or remoteContract or full-time$80K to $150K3+ years
You will work full stack: design and build features spanning front end, back end, data storage, and processing. New product modules and services from scratch, or evolve existing ones, guided by real healthcare data and workflows. You will integrate large language models, process large clinical and claims datasets, apply traditional ML where it works better, and build tooling around AI-driven flows.
What you will do
- Design and build features across React and TypeScript on the front end, Python or Node on the back end, and the data layer that connects them.
- Build new product modules from scratch, or evolve existing ones, against real healthcare data and workflows.
- Integrate LLMs, process large clinical and claims datasets, apply traditional ML, and build tooling around AI-driven flows.
- Make architectural decisions across frameworks, data models, APIs, and storage. Balance performance, scalability, maintainability, and complexity.
- Collaborate directly with co-founders to turn product vision into a working, maintainable codebase.
What we are looking for
- At least 3 years of full-stack engineering experience, including substantial work with AI/ML.
- Strong skills across front end, back end, and databases. Demonstrated ability to design end-to-end systems.
- Experience integrating AI/ML: LLMs, data pipelines, long-form text processing, and traditional ML.
- Good design sense and architectural thinking. You understand trade-offs and choose wisely based on constraints.
- Comfort in an early-stage startup environment. Nimble, iterative, high ownership.
- Bonus: prior startup or founder experience. We value entrepreneurial thinking, self-direction, and a willingness to wear multiple hats.
Machine Learning Engineer
San Francisco or remoteContract or full-time$80K to $150K3+ years
You will build and own production machine learning systems end to end, from data modeling and feature engineering to training, evaluation, deployment, and monitoring. The data is messy real-world healthcare data: claims, EHR, pharmacy, lab, ADT. The problems are ranking, prioritization, and prediction, where the model output drives a real human or autonomous workflow.
What you will do
- Build and own production ML systems end to end: data modeling, feature engineering, training, evaluation, deployment, and monitoring.
- Design and implement data pipelines that turn raw, messy real-world healthcare data into reliable features.
- Train and evaluate models for ranking, prioritization, and prediction, for example identifying high-risk or high-priority members.
- Deploy models as reliable services or batch jobs, with clear versioning, monitoring, and rollback strategies.
- Make architectural decisions around model choice, evaluation metrics, retraining cadence, and system guardrails. Balance accuracy, explainability, reliability, and operational constraints.
- Collaborate directly with founders and engineers to translate product and operational needs into scalable, maintainable ML solutions.
What we are looking for
- At least 3 years building and deploying ML systems in production.
- Strong foundation in ML for structured (tabular) data: feature engineering, regression or classification models, ranking or prioritization.
- Experience with the full ML lifecycle: data prep, train/test, evaluation, deployment, retraining, monitoring.
- Solid backend engineering skills: production-quality code, services or batch jobs, databases, data pipelines.
- Good system design instincts. You understand trade-offs between model complexity, reliability, latency, and maintainability.
- Ability to clearly explain modeling choices, assumptions, and limitations to non-ML stakeholders.
- Bonus: healthcare or operational decision-support systems, LLMs in production (RAG, fine-tuning, structured prompting), model monitoring and data drift tooling.