Staff Machine Learning Engineer, Sprinter Health
$220-270k
Offers Equity
More information about location
4+ days a week in office
On demand, at-home health tests
Posted today
On demand, at-home health tests
201-500 employees
Posted today
$220-270k
Offers Equity
More information about location
4+ days a week in office
201-500 employees
Company mission
To dramatically expand and untether access to healthcare by reimagining the patient experience—delivered at home and powered by technology for scale.
Role
Who you are
- This role is ideal for a staff-level, hands-on engineer who thinks in systems, has built ML infrastructure from the ground up, and knows how to right-size solutions for a rapidly growing startup
- You should be someone who empowers the teams around you, accelerates time to deployment, and knows what a model needs to be truly production-ready
- Spent 8+ years building production software, data systems, ML systems, platform infrastructure, or related technical systems
- Built and owned ML systems in production across training, serving, features, monitoring, and deployment
- Taken models from prototype or research stage into reliable, production-grade systems
- Built or meaningfully scaled ML infrastructure, MLOps platforms, model-serving systems, feature pipelines, or related infrastructure
- Designed systems that other engineers, data scientists, analysts, or product teams rely on
- Made architectural decisions around ML platform design, serving patterns, feature infrastructure, build versus buy, and operational standards
- Worked with cloud infrastructure, containers, CI/CD, orchestration, data pipelines, and production deployment workflows
- Built monitoring, observability, validation, or alerting for ML systems, data systems, or high-reliability production services
- Created reproducible workflows across data, features, models, training runs, deployments, or experiments
- Partnered closely with data science, applied science, data platform, product, operations, or backend engineering teams
- Operated in ambiguous environments where there was no existing playbook and technical decisions had a long half-life
- Balanced speed, simplicity, reliability, privacy, and long-term maintainability in production systems
- You’ve been an early ML engineer, founding ML engineer, or first ML infrastructure hire at a startup
- You’ve built ML infrastructure in a high-growth or operationally complex environment
- You have depth in large-scale model serving, feature infrastructure, LLM infrastructure, or real-time inference systems
- You have a background in backend engineering, data engineering, MLOps, platform engineering, or infrastructure engineering
- You have experience with feature stores, feature pipelines, or production data systems at scale
- You’ve helped interview, hire, mentor, or set the technical bar for ML engineers, platform engineers, or data engineers
- You’ve worked with healthcare data, PHI, HIPAA-aware systems, or other sensitive data environments
- You have experience with security, privacy, governance, or compliance considerations for production ML systems
What the job involves
- We’re looking for a Staff Machine Learning Engineer to be Sprinter’s first dedicated ML engineering hire and build the production systems that train, deploy, monitor, retrain, and serve machine learning models across the company
- This is a founding, first-of-function role
- You will define the blueprint for how ML moves from prototype to production at Sprinter, including our training and inference pipelines, serving patterns, feature workflows, monitoring, validation, retraining, and model governance practices
- You’ll work closely with engineering, data, product, operations, and applied science teams to turn models into reliable systems the company can depend on
- That includes serving predictions through APIs and batch jobs, building clean interfaces between data and product systems, and implementing the observability needed to catch drift, data quality issues, latency problems, cost regressions, and silent model degradation before they impact patients or operations
- Just as importantly, you’ll make the foundational calls that every future model and ML engineer will build on: build versus buy, serving architecture, feature paradigms, deployment standards, monitoring expectations, and the guardrails that allow us to move quickly without creating fragile systems
- As the function grows, you will have the opportunity to shape the team, define the technical bar, and help build the ML engineering foundation for Sprinter
- Build and lead Sprinter’s ML engineering function as the company’s first dedicated ML engineering hire
- Define Sprinter’s ML platform and deployment paradigm across training, serving, features, monitoring, retraining, and governance
- Make foundational build-versus-buy, architecture, tooling, and platform decisions that future models and engineers will build on
- Design and build production training and inference pipelines that are reliable, observable, and maintainable
- Package models for deployment and serve predictions through APIs, batch jobs, or other production workflows
- Build clean interfaces between data systems, models, and product systems so ML can be consumed safely and reliably
- Maintain feature pipelines and ensure features remain fresh, correct, and consistent between training and serving
- Implement monitoring for model performance, drift, data quality, latency, cost, reliability, and production behavior
- Prevent training-serving skew, silent degradation, and model regressions before they become production issues
- Automate retraining, validation, deployment, rollback, and other production ML workflows where appropriate
- Establish reproducibility, versioning, model governance, and operational readiness practices as company defaults
- Partner with engineering, data platform, product, operations, and applied science teams to productionize models and improve handoffs
- Write design docs, define technical standards, and bring the broader engineering organization along on key ML infrastructure decisions
- Set the technical bar for ML engineering by helping interview, mentor, and eventually hire engineers who follow
- You decide what the pattern should be and bring the rest of the organization along
- You reach for the simplest system that works, adding complexity only when the value justifies it
- You know what it takes to make a model production-ready and can communicate those requirements clearly
- You are an accelerator for applied science, data, product, and engineering teams, not a gatekeeper
- You build interfaces that make models easy to consume and hard to misuse
- You prevent silent degradation before it becomes an incident
- You create standards that help future engineers move faster
- You raise the technical bar for everyone who joins the function after you
- Deciding what Sprinter’s serving and feature paradigms should be and writing the design docs behind those decisions
- Hardening a training pipeline or batch-inference workflow
- Productionizing a model handed off from another team
- Debugging a model-serving issue or production data quality problem
- Reviewing feature freshness, model performance, drift, latency, or cost
- Building validation and rollback workflows for model deployments
- Partnering with product and operations teams to understand how model behavior impacts real-world workflows
- Interviewing a candidate, mentoring an engineer, or setting a new technical standard for the ML engineering function
Application process
- 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: Technical experience, first-of-function fit, and ML infrastructure depth
- Hands-on Technical Assessment: Practical ML engineering, production systems, and implementation ability
- Onsite Interview: Systems design, technical case study, behavioral interview, and lunch with the team
- References: Validation of performance, judgment, and working style
Our take
Sprinter Health offers on-demand health checks and lab draws by sending nurses and phlebotomists (employed by Sprinter itself) into the home. Amongst its services are blood draws and urine samples, mobile imaging and vitals checks, as well as assistance with telehealth consultations.
The rise in remote, digital health solutions is helping to bring greater access to care, but it’s also created a need for last-mile, in-person services. Indeed, while the last year or so has actually seen a marked drop in investment in digital health, home-based care providers are still going strong.
There’s no doubt that the COVID-19 pandemic had a lot to do with this, but it appears that the demand for in-home services is here to stay. If anything, they’re something of a throwback to decades gone by when house calls were a regular occurrence.
Despite only emerging from stealth in late 2021, Sprinter is very well funded and has been quickly building up its headcount in California, with a long-term focus on national expansion.
Steph
Company Specialist at Welcome to the Jungle
Insights
36% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
May 2025
$55m
SERIES B
Oct 2021
$33m
SERIES A
Total funding: $92.6m
Company benefits
- Free lunch everyday and fully-stocked microkitchens and coffee/tea bar
- Team offsites, game hours, tech talks, and design sprints
- Health insurance benefits (PPO / HMO / HSA, 100% of family premiums paid)
- Dental / vision / wellness benefits
- 401k matching
- Unlimited PTO
- Flexible work-from-home policy for work/life balance (4 days in-office, accommodation for occasional WFH days)
- Relocation assistance
- Generous parental leave (4 months for the birthing parent, 3 months for a partner, 100% paid)
Company HQ
The Castro, San Francisco, CA
Leadership
Max Cohen
(Co-Founder & CEO)Worked at Google from 2011 to 2014 as a Product Manager, before joining Oculus VR as VP of Mobile, a few months prior to Facebook's acquisition of the company. Continued at Oculus VR until 2019 as Head of Mobile, Head of Oculus Product for Mobile and Platform, and Head of AR/VR Media Product.
Cameron Behar
(Co-Founder & CTO)Like Sprinter Co-founder Max Cohen, previously worked for Google and Facebook. Worked as a Software Engineer and Tech Leads across a number of verticals at Google. Worked at Oculus after Facebook's acquisition as a Tech Lead Manager and Engineering Manager.
Salary benchmarks
We don't have enough data yet to provide salary benchmarks for this role.
Submit your salary to help other candidates with crowdsourced salary estimates.
Share this job
View 19 more jobs at Sprinter Health