Applied Scientist, AI

Sprinter Health • San Francisco, CA

2d • $180,000 - $260,000 • Hybrid

About The Position

We’re looking for an Applied Scientist, AI to turn messy, high-stakes healthcare problems into machine learning models and AI systems that improve access to care and help Sprinter operate more effectively. This role sits at the intersection of research, product, engineering, and clinical operations. You’ll take ambiguous product and operational problems and turn them into well-scoped prediction, ranking, optimization, NLP, or LLM-based tasks. You’ll build strong baselines, design honest evaluations, run careful error analysis, and iterate toward models that can improve real-world outcomes. The right person for this role combines scientific rigor with a deployment-oriented mindset. You should care deeply about evaluation, leakage, bias, confounding, and whether offline results actually translate into production impact. You should also be able to partner closely with ML engineering to productionize models, work with clinicians and subject-matter experts to validate assumptions, and explain model behavior, uncertainty, and limitations clearly to product and leadership. This role is ideal for a scientist-engineer who can move fluidly between data exploration, modeling, experimentation, error analysis, stakeholder partnership, and production handoff.

Requirements

• Built, evaluated, and iterated on machine learning or AI models for real-world use cases
• Turned ambiguous business, product, clinical, or operational problems into measurable modeling tasks
• Designed rigorous offline evaluations, experiments, or analyses that informed production or product decisions
• Worked with messy real-world datasets where labels, outcomes, and causal relationships are imperfect
• Used statistical reasoning, experimental design, and error analysis to understand model performance
• Built models using Python and standard ML or AI tooling such as PyTorch, scikit-learn, NumPy, pandas, Polars, Hugging Face, Matplotlib, or similar
• Compared modeling approaches and made pragmatic decisions about when to use traditional ML, LLMs, heuristics, or simpler baselines
• Communicated model performance, limitations, tradeoffs, and uncertainty to technical and non-technical stakeholders
• Partnered with engineering, product, data, operations, clinical, or domain experts to move models closer to production impact
• Operated with enough engineering depth to run experiments end to end and self-serve deployments or production handoffs when needed
• Used AI coding assistants such as Claude Code, Cursor, or similar tools as part of your development workflow

Nice To Haves

• You have an MS or PhD in computer science, statistics, machine learning, applied math, operations research, biomedical informatics, epidemiology, or a related quantitative field
• You have exceptional applied experience that substitutes for formal graduate training
• You have depth in LLMs, ranking, NLP, uncertainty quantification, causal inference, optimization, or healthcare AI
• You’ve shipped models that reached production and had measurable real-world impact
• You’ve worked with healthcare data such as claims, EHR, clinical notes, scheduling, utilization, quality, risk, or patient engagement data
• You have experience working with PHI, HIPAA-aware systems, or other sensitive regulated data
• You know when traditional ML approaches are likely to outperform LLMs, and when LLMs are the right tool
• You have experience collaborating with clinicians, clinical operations teams, or other high-stakes domain experts
• You’ve worked in a startup or fast-moving applied environment where ambiguity, speed, and rigor all mattered

Responsibilities

• Turn ambiguous healthcare, product, and operational problems into well-posed ML, AI, ranking, optimization, NLP, or LLM-based tasks
• Build strong baselines and improve on them efficiently using the right modeling approach for the problem
• Develop models across traditional ML, deep learning, NLP, and LLM-based approaches where appropriate
• Design offline and online evaluations that are honest, measurable, and predictive of real-world impact
• Choose metrics suited to imbalanced, delayed, noisy, and partially observed healthcare outcomes
• Run careful error analysis and use it to improve model quality, product fit, and operational usefulness
• Identify label leakage, selection bias, confounding, and other data artifacts before they reach production
• Explore messy real-world data, assess label quality, and determine whether a problem is ready for modeling
• Partner with ML engineering to productionize models reliably and define what production-readiness requires
• Work with clinical stakeholders and subject-matter experts to validate assumptions, review model errors, and understand edge cases
• Explain model tradeoffs, uncertainty, limitations, and expected impact clearly to product, operations, clinical, and leadership teams
• Write experiment docs, summarize findings, and help teams make informed decisions about when and how to deploy AI systems
• Pressure-test whether results are real, robust, and useful before recommending production use

Benefits

• Meaningful pre-IPO equity
• Medical, dental, and vision plans 100% paid for you and your dependents
• Flexible PTO + 10 paid holidays per year
• 401(k) with match
• 16-week parental leave policy for birthing parent, 8 weeks for all other parents
• HSA + FSA contributions
• Life insurance, plus short and long-term disability coverage
• Free daily lunch in-office
• Annual learning stipend
• Relocation assistance

What This Job Offers

• Job Type: Full-time
• Career Level: Senior