Applied Scientist (A.I)

Join Sprinter, a healthcare technology company, as an Applied Scientist, AI. In this role, you will turn complex healthcare problems into machine learning models and AI systems that improve access to care and enhance operational efficiency. You will work closely with various teams, including research, product, engineering, and clinical operations, to develop and deploy impactful AI solutions.

• Transform ambiguous healthcare, product, and operational problems into well-posed machine learning, AI, ranking, optimization, NLP, or LLM-based tasks.
• Build strong baselines and improve on them efficiently using the right modeling approach for the problem, including traditional ML, deep learning, NLP, and LLM-based approaches.
• Design offline and online evaluations that are honest, measurable, and predictive of real-world impact, and run careful error analysis to improve model quality.
• This role is ideal for a scientist-engineer who can move fluidly between data exploration, modeling, experimentation, error analysis, stakeholder partnership, and production handoff
• Turned ambiguous business, product, clinical, or operational problems into measurable modeling tasks
• Used statistical reasoning, experimental design, and error analysis to understand model performance
• Built, evaluated, and iterated on machine learning or AI models for real-world use cases
• 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
• Partnered with engineering, product, data, operations, clinical, or domain experts to move models closer to production impact
• 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
• 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
• What you have done:
• 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’ve shipped models that reached production and had measurable real-world impact
• You have depth in LLMs, ranking, NLP, uncertainty quantification, causal inference, optimization, or healthcare AI
• 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