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Remote Machine Learning Compiler Engineer Jobs in San Francisco, CA

EvenUp

Senior Machine Learning Engineer

San Francisco, CA · Remote

$196K - $265K/yr

Senior Machine Learning Engineer At EvenUp, we leverage cutting-edge AI to bring fairness and accessibility to the legal system. Tackling the most complex legal document challenges requires expertise ...

Care.coach

Lead Machine Learning Engineer

Millbrae, CA · On-site +1

$119K - $156K/yr

... key engineering leadership role -- Minimum Requirements: * Doctorate in a related field * 8+ years of experience (including any applicable work in grad school) developing machine learning ...

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All JobsMachine Learning Compiler Engineer JobsRemote Machine Learning Compiler Engineer JobsRemote Machine Learning Compiler Engineer Jobs in San Francisco, CA

Remote Machine Learning Compiler Engineer information

See San Francisco, CA salary details

$88.4K

$197.3K

$241.5K

How much do remote machine learning compiler engineer jobs pay per year?

As of Sep 6, 2026, the average yearly pay for remote machine learning compiler engineer in San Francisco, CA is $197,270.00, according to ZipRecruiter salary data. Most workers in this role earn between $168,500.00 and $241,500.00 per year, depending on experience, location, and employer.

What is a remote machine learning compiler engineer?

A Remote Machine Learning Compiler Engineer is a software engineer who specializes in developing and optimizing compilers specifically for machine learning workloads, while working from a remote location. Their primary responsibilities include designing and implementing compiler features that translate machine learning models into efficient code for various hardware platforms, such as CPUs, GPUs, or specialized accelerators. They collaborate closely with machine learning researchers, hardware engineers, and software developers to ensure high performance and compatibility. In addition to strong programming skills, they typically require expertise in compiler theory, machine learning frameworks, and hardware architectures. This role allows for flexible, location-independent work while contributing to cutting-edge AI technologies.

How does a remote machine learning compiler engineer typically collaborate with cross-functional teams to optimize model deployment?

As a Remote Machine Learning Compiler Engineer, you will frequently collaborate with data scientists, hardware engineers, and software developers to ensure that machine learning models are efficiently compiled and deployed on target platforms. Communication often takes place through virtual meetings, code reviews, and shared documentation tools. You'll be responsible for translating research models into optimized code, troubleshooting performance bottlenecks, and integrating feedback from various stakeholders. Effective teamwork is crucial, as the success of deployments often depends on iterative feedback and close alignment with both the ML research and hardware teams.

What are the key skills and qualifications needed to thrive as a remote machine learning compiler engineer, and why are they important?

To thrive as a Remote Machine Learning Compiler Engineer, you need a strong background in computer science, proficiency in programming languages like C++ and Python, and expertise in compiler theory and machine learning frameworks. Familiarity with ML compilers such as TVM or XLA, and experience using version control and CI/CD systems are commonly required, along with a relevant bachelor's or master's degree. Outstanding problem-solving, collaboration, and communication skills are essential for working effectively in distributed teams and across technical domains. These skills and qualities enable the development of efficient, scalable ML solutions that bridge software and hardware, ensuring high performance and innovation.

What is the difference between Remote Machine Learning Compiler Engineer vs Remote Data Scientist?

AspectRemote Machine Learning Compiler EngineerRemote Data Scientist
Required CredentialsBachelor's or Master's in Computer Science, Software Engineering, or related fields; knowledge of compiler design and ML frameworksBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in programming, statistics, and data analysis
Work EnvironmentPrimarily software development, compiler optimization, and ML model deploymentData analysis, model building, and interpretation of results
Industry UsageTech companies, AI startups, hardware firms focusing on ML hardware accelerationTech, finance, healthcare, and research organizations

While both roles involve working with machine learning, the Remote Machine Learning Compiler Engineer focuses on developing and optimizing compilers for ML models, whereas the Remote Data Scientist concentrates on analyzing data and building predictive models. The roles share some technical skills but differ in their core responsibilities and work environments.

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Remote Machine Learning Compiler Engineer jobs near you

Machine Learning Engineer

Sprinter Health

San Francisco, CA • Remote

$140K - $200K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 10 days ago

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 + 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

Compensation Range: $140K - $200K