Machine Learning Engineer at Pelica | Y Combinator

Open menuAboutWhat Happens at YC?ApplyYC Interview GuideFAQPeopleYC BlogCompaniesStartup DirectoryFounder DirectoryLaunch YCLibraryPartnersResourcesStartup SchoolNewsletterRequests for StartupsFor InvestorsVerify FoundersHacker NewsBookfaceSafeFind a Co-FounderStartup JobsLog inApplyPelicaTransforming healthcare operations with AI agentsMachine Learning Engineer$80K - $150K•San Francisco, CA, US / RemoteJob typeContractRoleEngineering, Machine learningExperience1+ yearsVisaUS citizenship/visa not requiredSkillsAmazon Web Services (AWS), Python, Machine Learning, Data Modeling, Data AnalyticsConnect directly with founders of the best YC-funded startups.Apply to role ›Lalit KunduFounderLalit KunduFounderAbout the roleAbout Us
Pelica Health is the operating system for value-based care. 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, across risk adjustment, Quality and Stars, pharmacy and Part D, provider network, and care management.
Pelica was founded by former engineering and AI leaders from Google and YouTube, including co-founders who built large-scale infrastructure and machine learning systems. You will work alongside people who built massive systems at scale, a chance to learn a lot and contribute meaningfully from day one. We are backed by Y Combinator.
We believe in solving hard problems together as a team, iterating quickly, and building software with long-term thinking and ownership.
What You'll Do

Build and own production machine learning systems end-to-end, from data modeling and feature engineering to training, evaluation, deployment, and monitoring.
Design and implement data pipelines that turn raw, messy real-world healthcare data into reliable features for machine learning models.
Train and evaluate models for ranking, prioritization, and prediction problems, for example identifying high-risk or high-priority cases.
Deploy models into production as reliable services or batch jobs, with clear versioning, monitoring, and rollback strategies.
Work closely with backend engineers and product leaders to integrate machine learning into real workflows and decision-making systems.
Make architectural decisions around model choice, evaluation metrics, retraining cadence, and system guardrails, balancing accuracy, explainability, reliability, and operational constraints.
Collaborate directly with founders and engineers to translate product and operational needs into scalable, maintainable machine learning solutions.

What We're Looking For

At least 3 years of experience building and deploying machine learning systems in production.
Strong foundation in machine learning for structured (tabular) data, including feature engineering, regression or classification models, and ranking or prioritization problems.
Experience with the full machine learning lifecycle: data preparation, train/test splitting, evaluation, deployment, retraining, and monitoring.
Solid backend engineering skills: writing production-quality code, building services or batch jobs, and working with databases and data pipelines.
Good system design instincts. You understand trade-offs between model complexity, reliability, latency, scalability, and maintainability.
Comfort working in a fast-paced startup environment with high ownership and ambiguity.
Ability to clearly explain modeling choices, assumptions, and limitations to non-machine-learning stakeholders.

Bonus:

Experience working with healthcare or operational decision-support systems.
Experience building or integrating LLM systems in production, such as retrieval-augmented generation, fine-tuning, or structured prompting workflows.
Prior startup experience or founder mindset. We value ownership, pragmatism, and bias toward shipping.
Experience with model monitoring, data drift detection, or ML infrastructure tooling.

Why Join

Learn from seasoned Google and YouTube engineers who have operated at massive scale. You will build similar systems and learn best practices, scale thinking, and software design deeply.
High impact: on a small, ambitious team, your work shapes architecture, product direction, and core features. You will have real ownership and see results quickly.
Grow fast: you will work across AI/ML pipelines, system architecture, data modeling, and product-level decisions, a fast track to becoming a senior engineer or technical lead.
Meaningful work: we are bringing modern AI to the hardest problems in healthcare, helping the teams closest to patients close care gaps and improve outcomes. If you enjoy building reliable, scalable systems that matter, this is for you.

About PelicaAI operating system for value based care organizations.
PelicaFounded:2025Batch:P25Team Size:5Status:ActiveLocation:San Francisco FoundersLalit Kundu FounderLalit Kundu FounderCatherine Zhao FounderCatherine Zhao FounderFooterY CombinatorMake something people want.ProgramsYC ProgramStartup SchoolWork at a StartupCo-Founder MatchingResourcesStartup DirectoryStartup LibraryInvestorsDemo DaySafeHacker NewsLaunch YCYC DealsCompanyYC BlogContactPressPeopleCareersPrivacy PolicyNotice at CollectionSecurityTerms of UseTwitterTwitterFacebookFacebookInstagramInstagramLinkedInLinkedInYoutubeYouTube© 2026 Y Combinator

The opportunity as a Machine Learning Engineer at Pelica involves working within a company that functions as the operating system for value-based care, unifying disparate healthcare data such as claims, electronic health records, pharmacy, lab, and ADT data into a singular live record for each member. This unified data forms the basis for an AI copilot deployed across various facets of care management, including risk adjustment, quality measures, provider networks, and care management processes. Pelica was founded by individuals with deep experience in engineering and artificial intelligence from organizations like Google and YouTube, suggesting an environment where high-scale systems and advanced machine learning principles are central to the mission.

The core responsibilities of the role focus on the entire machine learning system lifecycle. Engineers will be tasked with building and owning production machine learning systems end-to-end, which requires proficiency in data modeling and feature engineering, model training, evaluation, deployment, and ongoing monitoring. A significant part of the work involves designing and implementing robust data pipelines capable of transforming raw, complex healthcare data into reliable features suitable for machine learning models. Furthermore, the role demands the ability to train and evaluate predictive models, particularly for ranking, prioritization, and risk identification, such as flagging high-risk cases. Deployment strategies must incorporate clear versioning, monitoring, and rollback mechanisms to ensure model reliability in live services or batch jobs.

Success in this position also requires strong backend engineering capabilities, as the work involves writing production-quality code, building necessary services or batch jobs, and managing interactions with databases and data pipelines. Architectural decision-making is crucial, requiring an understanding of trade-offs among model complexity, required reliability, latency targets, scalability, and overall maintainability. The engineer must collaborate closely with product leaders and backend teams to integrate these machine learning components seamlessly into real workflows and decision-making systems.

The ideal candidate possesses at least three years of experience in building and deploying machine learning systems in a production setting, supported by a strong foundation in machine learning applied to structured, tabular data, including expertise in feature engineering and classification or ranking problems. Essential competencies include familiarity with the complete machine learning lifecycle, encompassing data preparation, splitting, evaluation, retraining, and monitoring. Beyond technical skills, the role requires good system design instincts to navigate complex technical constraints and the ability to clearly articulate modeling choices, assumptions, and limitations to non-specialist stakeholders. The context of the role is rooted in a fast-paced startup environment, demanding comfort with high ownership and ambiguity, alongside the capacity to operate with pragmatism and a bias toward shipping functional solutions.

Bonus experience is highly valued, particularly any background involving healthcare or operational decision-support systems. Experience building or integrating large language model systems in production, such as using retrieval-augmented generation or structured prompting workflows, is also beneficial. Furthermore, familiarity with model monitoring, techniques for detecting data drift, and general machine learning infrastructure tooling would add significant value. Joining Pelica offers the prospect of learning from engineers who have operated at massive scale, contributing to a high-impact product that directly addresses critical healthcare challenges by building reliable and scalable systems.