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About This Role
About Sprinter Health:
At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can’t get to a doctor’s office. For many, the ER becomes their first touchpoint with the healthcare system—driving over $300B in avoidable costs every year.
By using the same technologies that power leading marketplace and last\-mile platforms, we deliver care where people are, especially those who need it most. So far, we’ve supported more than 2 million patients across 22 states, completed 130,000\+ in\-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi\-year runway.
About the Role
We’re looking for an AI Research Scientist to advance the methodological frontier of AI in healthcare. This role is ideal for someone who has demonstrated strong research taste, deep technical foundations, and the ability to turn open\-ended problems into rigorous scientific contributions.
You will develop and own a research agenda aligned with Sprinter’s company strategy. Your work may include novel architectures, new training or evaluation techniques, long\-horizon research bets, peer\-reviewed validation studies, patents, and methods that ultimately graduate into production systems.
The ideal candidate is deeply technical, scientifically rigorous, and excited to collaborate closely with clinicians, product leaders, and applied AI teams. You understand that healthcare validation standards are higher than benchmark culture alone, and you are energized by the opportunity to produce research that is both scientifically meaningful and practically impactful.
Hybrid \& Office Experience
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.
What you will do:
### Research Agenda \& Scientific Contribution
- Develop and own a research agenda aligned with Sprinter’s long\-term AI and company strategy.
- Identify open problems, position them against the literature, and design experiments that isolate meaningful contributions.
- Develop novel methods, architectures, training approaches, evaluation techniques, or validation frameworks.
- Produce publications, patents, peer\-reviewed validation studies, and other evidence artifacts.
- Translate promising research into methods and tools that applied teams can use in production.
### Technical Leadership
- Raise the scientific bar across applied AI and engineering teams.
- Review methodologies, evaluation approaches, and experimental designs.
- Advise teams on hard technical decisions, especially around model performance, reliability, evaluation, uncertainty, and validation.
- Help determine whether a result is meaningful, reproducible, or an artifact.
- Mentor applied researchers and engineers on rigorous ML research practices.
### External Presence \& Collaboration
- Maintain an external research presence through publications, talks, academic collaborations, and participation in relevant research communities.
- Collaborate with clinical partners on validation studies, including work that may involve IRB review, data governance, external validation, or prospective evaluation.
- Partner cross\-functionally with Product, Clinical, Engineering, and Leadership teams to ensure research priorities map to meaningful company and patient impact.
What you have done:
- Demonstrated ability to produce novel research, including identifying open problems, designing rigorous experiments, and writing work to a peer\-review standard.
- Deep ML foundations and genuine depth in at least one relevant area, such as LLMs, agents, uncertainty, causality, multimodal learning, clinical AI, or related fields.
- Strong engineering ability, including the ability to run your own experiments at scale.
- Strong research taste and the ability to distinguish incremental work from meaningful methodological contribution.
- Comfort working in open\-ended, ambiguous environments where the right research direction may need to be shaped from first principles.
- Interest in clinical collaboration and applied healthcare impact.
- Understanding of healthcare validation standards, including the importance of external validation, prospective evaluation, data governance, and real\-world deployment constraints.
What gives you an edge:
- First\-author publications at top technical venues such as NeurIPS, ICML, ICLR, ACL, or related conferences.
- Publications in leading clinical AI or healthcare venues such as Nature Medicine, NEJM AI, npj Digital Medicine, CHIL, MLHC, or similar.
- Experience in academia, industry research labs, or research\-heavy teams at AI\-native healthcare companies.
- Experience collaborating with clinicians, clinical researchers, or healthcare operators.
- Familiarity with IRB processes, clinical data governance, or healthcare model validation.
- Dual literacy across machine learning and clinical collaboration.
Interview Process:
- We aim to complete the interview process between 2–3 weeks. It will usually consist of:
+ Recruiter Screen (30 minutes)
+ Hiring Manager Introduction (30 minutes)
+ Hands\-on\-Keys Technical Assessment (1 hour)
+ Onsite Interview: Systems Design / Technical Case Study \+ Research Presentation \+ Behavioral Interview \+ Lunch with the Team (4 hours)
+ References
What we offer:
- 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
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.
Compensation Range: $160K \- $220K
Salary Context
This $160K-$220K range is above the median for Research Scientist roles in our dataset (median: $183K across 83 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 3,708 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Sprinter Health, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $222,200 based on 197 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($190K) sits 14% below the category median. Disclosed range: $160K to $220K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Sprinter Health AI Hiring
Sprinter Health has 5 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Menlo Park, CA, US, San Francisco, CA, US. Compensation range: $220K - $270K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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