Applied Scientist, AI

We are a hybrid company based in the Bay Area with offices in both San Francisco and Menlo Park. We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days.

We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most.

Lunch is provided every day, and the entire team takes an hour to eat together. It’s one of the ways we stay connected outside of meetings. You’ll usually find us playing a board game before getting back to work.

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

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

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

In this role, you might spend your time:

Exploring data and labels for a new healthcare or operational problem

Turning an ambiguous product question into a measurable modeling task

Building and comparing models, then running error analysis

Reviewing misclassified or low-confidence cases with a clinical subject-matter expert

Designing an offline evaluation that is more likely to predict online or real-world success

Partnering with ML engineering to prepare a model for deployment

Writing an experiment doc and presenting findings to product and leadership

Pressure-testing whether a result is real or an artifact of the data

Comparing a simple baseline, traditional ML model, and LLM-based approach to determine what is most useful

Investigating why model performance differs across populations, workflows, labels, or operational contexts

We aim to complete the interview process within 2–3 weeks. It will usually consist of:

Recruiter Screen: Background fit, motivation, and compensation alignment

Hiring Manager Interview: Applied science experience, modeling depth, and healthcare/product orientation

Hands-on Technical Assessment: Practical modeling, evaluation, error analysis, and scientific judgment

Onsite Interview: Technical case study, research or project presentation, behavioral interview, and lunch with the team

References: Validation of performance, judgment, and working style

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

Sprinter Health is an equal opportunity employer. We value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or other protected classes.

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