Applied Scientist, AI

Sprinter Health

Applied Scientist developing AI models to improve healthcare access and operational efficiency at Sprinter Health. Collaborating across research, product, engineering, and clinical operations teams.

Posted 7/20/2026full-timeSan Francisco • California • 🇺🇸 United StatesMid-LevelSenior💰 $180,000 - $260,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and evaluating machine learning and AI models for healthcare applications, with a strong focus on experimental design, error analysis, and collaboration with cross-functional teams. Proficient in utilizing Python and various ML tools to address complex, real-world problems and ensure production readiness.

Highest-signal resume keywords

Machine Learning Model DevelopmentStatistical ReasoningPython ProgrammingNLP and LLM TechniquesExperimental Design

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningArtificial IntelligenceDeep LearningNatural Language ProcessingModel EvaluationError AnalysisData AnalysisStatistical ReasoningModel OptimizationCausal Inference

Soft Skills

CommunicationCollaborationProblem-SolvingCritical ThinkingStakeholder Engagement

Tools & Technologies

PythonPyTorchScikit-learnNumPyPandasPolarsHugging FaceMatplotlibClaude CodeCursor

Industry Keywords

HealthcareModel ProductionizationData Quality AssessmentImbalanced OutcomesOperational Usefulness

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorchScikit-Learn

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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

Benefits

Comp & perks

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