Pelica (YC P25) Is Hiring
Source: Hacker News
Machine Learning Engineer
Location: San Francisco, CA, US / Remote
Salary: $80 K – $150 K
Job Details
- Type: Contract
- Role: Engineering – Machine Learning
- Experience Required: 1 + year
- Visa: US citizenship / visa not required
Required Skills
- Amazon Web Services (AWS)
- Python
- Machine Learning
- Data Modeling
- Data Analytics
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About the Founder
Lalit Kundu – Founder
About Us
Pelica Health is the operating system for value‑based care. We unify claims, EHR, pharmacy, lab, and ADT data into a single, live record per member, then place an AI copilot beside every team that relies on it—across risk adjustment, Quality & Stars, pharmacy & Part D, provider networks, and care management.
Our Team
- Founded by former engineering and AI leaders from Google and YouTube.
- Co‑founders have built large‑scale infrastructure and machine‑learning systems.
- You’ll work alongside people who have built massive, production‑grade systems—offering a chance to learn quickly and make meaningful contributions from day one.
Backing
- Backed by Y Combinator.
Our Philosophy
- Solve hard problems together as a team.
- Iterate quickly.
- Build software with long‑term thinking and ownership.
What You’ll Do
- Build and own production machine‑learning systems end‑to‑end, covering data modeling, feature engineering, training, evaluation, deployment, and monitoring.
- Design and implement data pipelines that transform raw, messy healthcare data into reliable features for ML models.
- Train and evaluate models for ranking, prioritization, and prediction tasks (e.g., identifying high‑risk or high‑priority cases).
- Deploy models to production as robust services or batch jobs, with clear versioning, monitoring, and rollback strategies.
- Collaborate with backend engineers and product leaders to embed machine learning into real workflows and decision‑making systems.
- Make architectural decisions regarding model selection, evaluation metrics, retraining cadence, and system guardrails, balancing accuracy, explainability, reliability, and operational constraints.
- Work directly with founders and engineers to translate product and operational needs into scalable, maintainable machine‑learning solutions.
What We’re Looking For
- Experience: Minimum 3 years building and deploying machine‑learning systems in production.
- Domain expertise: Strong foundation in ML for structured (tabular) data—including feature engineering, regression or classification models, and ranking or prioritization problems.
- ML lifecycle: Proficient in data preparation, train/test splitting, evaluation, deployment, retraining, and monitoring.
- Backend engineering: Production‑quality code, services or batch jobs, and experience with databases and data pipelines.
- System design: Good instincts for balancing model complexity, reliability, latency, scalability, and maintainability.
- Startup mindset: Comfortable in a fast‑paced environment with high ownership and ambiguity.
- Communication: Ability to clearly explain modeling choices, assumptions, and limitations to non‑ML stakeholders.
Bonus
- Experience working with healthcare or operational decision‑support systems.
- Building or integrating LLM systems in production (e.g., retrieval‑augmented generation, fine‑tuning, structured prompting workflows).
- Prior startup experience or a founder mindset—valuing ownership, pragmatism, and a bias toward shipping.
- Experience with model monitoring, data‑drift detection, or ML‑infrastructure tooling.
Why Join
-
Learn from seasoned Google and YouTube engineers
Work with experts who have built systems at massive scale. You’ll design similar architectures, adopt best‑practice patterns, and deepen your understanding of scalability and software design. -
High impact
On a small, ambitious team your contributions shape the architecture, product direction, and core features. You’ll own end‑to‑end work and see the results of your efforts quickly. -
Fast growth
Gain experience across AI/ML pipelines, system architecture, data modeling, and product‑level decisions—a fast track toward senior engineer or technical‑lead roles. -
Meaningful work
Help bring modern AI to the toughest problems in healthcare. Your reliable, scalable systems will enable teams closest to patients to close care gaps and improve outcomes.
AI operating system for value‑based care organizations