# Applied Scientist, AI **Company:** [Sprinter Health](https://hotfix.jobs/companies/sprinter-health) **Location:** San Francisco, CA **Role:** ML Engineering **Salary:** $180k – $260k/yr **Skills:** Machine Learning, Deep Learning, NLP, LLMs, PyTorch, scikit-learn, Python, pandas, hugging face, Causal Inference, uncertainty quantification **Posted:** 2026-07-20 > Build, evaluate, and productionize ML/AI models (including LLMs and NLP) that solve ambiguous healthcare, product, and operational problems at Sprinter Health. Requires strong experimentation, error analysis, stakeholder collaboration with clinicians, and focus on real-world impact, bias, and evaluation. ## Job Description ## What you will do - 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 ## What you have done - 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 ## What gives you an edge - 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 ## What makes you successful - You understand how ML models work under the hood and can explain them clearly to non-technical stakeholders - You focus relentlessly on impact and know that the simplest model is often the best one - You treat evaluation as one of the most important parts of model development - You notice when a metric is misleading, incomplete, or disconnected from real-world outcomes - You catch leakage, bias, and confounding that others miss - You move fluidly between modeling, error analysis, stakeholder partnership, and production handoff - You can hand a model to engineering and explain its limits to a clinician with equal clarity - You are comfortable with ambiguity and can adapt modeling approaches to problems that do not come with a playbook - You balance scientific rigor with the practical need to ship useful systems ## Similar roles - [Perception Engineer](https://hotfix.jobs/jobs/4b0279a4-3c15-4f99-aed7-f216ca5c51ee) - Applied Intuition - Sunnyvale, CA - $180k – $255k/yr - [Software Development Engineer in Test, Machine Learning](https://hotfix.jobs/jobs/b60fd6fe-7b14-41db-898a-cda6316f2477) - Zoox - Foster City, CA - $180k – $225k/yr - [Software Engineer, AI Platform](https://hotfix.jobs/jobs/7ddad000-3e1c-47cb-a45b-31d8e72abc4f) - 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