about us
Pelika Health is the operating system for value-based care. We integrate claims, EHR, pharmacy, lab and ADT data into one live record per member, then put an AI copilot next to each team relying on it in risk adjustment, quality and STARS, pharmacy and Part D, provider networks and care management.
Pelika was founded by former engineering and AI leaders from Google and YouTube, including co-founders who had built large-scale infrastructure and machine learning systems. You’ll work alongside the people who built systems at scale, with the opportunity to learn a lot from day one and make meaningful contributions. We are backed by Y Combinator.
We believe in solving hard problems together as a team, iterating rapidly, and building software with long-term thinking and ownership.
What would you do
- Build and own an end-to-end production machine learning system, from data modeling and feature engineering to training, evaluation, deployment, and monitoring.
- Design and implement data pipelines that transform raw, dirty 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 trusted 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, re-training cadence, and system guardrails while balancing accuracy, interpretability, reliability, and operational constraints.
- Collaborate directly with founders and engineers to translate product and operational requirements into scalable, maintainable machine learning solutions.
what we are 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 entire machine learning lifecycle: data preparation, train/test splits, 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 trends. You understand the 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 health care or operational decision-support systems.
- Experience building or integrating LLM systems into production, such as retrieval-enhanced generation, fine-tuning, or structured prompting workflows.
- Prior startup experience or founder mindset. We value ownership, practicality and bias towards shipping.
- Experience with model monitoring, data drift detection, or ML infrastructure tooling.
Why join?
- Learn from experienced Google and YouTube engineers who have worked extensively. You’ll build similar systems and learn best practices, scale thinking, and software design in depth.
- High impact: On a small, ambitious team, your work shapes the architecture, product direction, and core features. You’ll have real ownership and see results quickly.
- Move forward fast: You’ll work on AI/ML pipelines, systems architecture, data modeling, and product-level decisions, which is a fast track to becoming a senior engineer or technical lead.
- Meaningful Work: We’re bringing modern AI to the toughest problems in health care, helping teams closest to patients bridge care gaps and improve outcomes. If you love building reliable, scalable systems that matter, this is for you.
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