Machine Learning Engineer
San Francisco, CA · San Francisco, California, United States
- Pay
- Pay not listedSimilar roles state $165K–$240K
- Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting- Team
Sprinter Health › Engineering — team source
Department: Sprinter Health › Engineering
From the employer’s posting
Before you apply
- Sponsorship
Visa sponsorship not confirmed — sponsorship source
Do you or will you require Visa Sponsorship in the present or future?
From the employer’s application form
What you’ll work on
Full postingWe’re looking for an ML Engineer to build the production systems that train, deploy, monitor, retrain, and serve our machine-learning models reliably.
You will build training and inference pipelines, serve predictions through APIs and batch jobs, and stand up the monitoring that catches drift and silent degradation before they reach a patient or a partner.
Build and harden training and inference pipelines
Serve predictions through APIs or batch jobs
Monitor drift, data quality, latency, cost, and performance
Automate retraining and validation, with safe rollback
Productionize models handed off from other teams
From the employer’s posting
We’re looking for an ML Engineer to build the production systems that train, deploy, monitor, retrain, and serve our machine-learning models reliably. You sit between software engineering, data engineering, and modeling, and you make ML work in the real world and stay working.
You will build training and inference pipelines, serve predictions through APIs and batch jobs, and stand up the monitoring that catches drift and silent degradation before they reach a patient or a partner. You will turn the models that scientists prototype into systems the company can depend on.
Build and harden training pipelines.
Serve predictions through APIs or batch jobs with reliability in mind.
Monitor drift, data quality, latency, cost, and performance.
Automate retraining and validation, and design safe rollback.
Productionize models handed off from other teams.
What you’ll bring
All qualificationsCore experience
- Strong Python and software-engineering fundamentals
- Experience taking models from prototype to reliable production
- Experience with ML frameworks, data pipelines, and model serving
- Cloud infrastructure, containers, CI/CD, and orchestration
- Monitoring and observability, plus reproducibility and versioning
Qualification wording
Strong Python and software-engineering fundamentals.
Experience taking models from prototype to reliable production.
Experience with ML frameworks, data pipelines, and model serving.
Cloud infrastructure, containers, CI/CD, and orchestration.
Monitoring and observability, plus reproducibility and versioning across data, features, and models.
Tools in this posting
- Python
Source — Tool mentions in context
What you have done: - Strong Python and software-engineering fundamentals. - Experience with ML frameworks, data pipelines, and model serving.
Benefits in the posting
Full benefits wording- 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
From the employer’s posting.
About Sprinter Health
At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes.
In the employer’s words · Read in context · Company website ↗
Posting record
Report an errorOpen 3 monthsWe first saw this posting 94 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jul 28, 2026 · 94 days ago
- Recorded sightings
- 161
- Last seen by us
- Oct 30, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
Complete your application on jobs.ashbyhq.com. The employer’s form will show what is required.
Already applied? Track this application
Job description
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 ML Engineer to build the production systems that train, deploy, monitor, retrain, and serve our machine-learning models reliably. You sit between software engineering, data engineering, and modeling, and you make ML work in the real world and stay working.
You will build training and inference pipelines, serve predictions through APIs and batch jobs, and stand up the monitoring that catches drift and silent degradation before they reach a patient or a partner. You will turn the models that scientists prototype into systems the company can depend on.
The ideal candidate thinks in systems rather than notebooks, knows what a model needs to become production-ready, and builds clean interfaces between data, models, and product.
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:
Production ML Systems
Build and harden training pipelines.
Package models for deployment.
Serve predictions through APIs or batch jobs with reliability in mind.
Maintain feature pipelines and keep features fresh and correct.
Reliability & Observability
Monitor drift, data quality, latency, cost, and performance.
Automate retraining and validation, and design safe rollback.
Prevent training-serving skew and silent model degradation.
Collaboration & Craft
Productionize models handed off from other teams.
Build clean interfaces between data, model, and product systems.
Implement reproducibility, versioning, and model-governance artifacts..
What you have done:
Strong Python and software-engineering fundamentals.
Experience with ML frameworks, data pipelines, and model serving.
Experience taking models from prototype to reliable production.
Cloud infrastructure, containers, CI/CD, and orchestration.
Monitoring and observability, plus reproducibility and versioning across data, features, and models.
Comfort with security and privacy controls for sensitive data.
What gives you an edge:
Background in backend engineering, data engineering, MLOps, or platform engineering.
Experience with feature stores or feature pipelines at scale.
Familiarity with healthcare data and PHI-aware systems
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 + 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
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
San Francisco, California, United States
The ideal candidate thinks in systems rather than notebooks, knows what a model needs to become production-ready, and builds clean interfaces between data, models, and product. 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.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
See how this role fits your experience
Add your resume to compare the role’s scope, tools and requirements with your experience.