Machine Learning Engineer

About 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.