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Remote Machine Learning Compiler Engineer Jobs in San Francisco, CA

Elicit

Machine Learning Engineer

Oakland, CA · On-site +1

We invite you to help us build that future. (See how people use Elicit today on Twitter; explore our vision in the roadmap.) About the role As a Machine Learning Engineer at Elicit, you'll build ...

Higgsfield

Machine Learning Engineer, Ads

San Francisco, CA · Remote

$165K - $230K/yr

About the role We're looking for exceptional Machine Learning Engineers focused on Ads to help take Higgsfield's advertising platform to the next level. You'll work at the intersection of large-scale ...

New

PlacingIT

Head of Machine Learning - 1811

San Francisco, CA · Remote

$180K - $250K/yr

Remote (United States) Employment Type: Direct Hire - Full-Time Compensation: $180K-$250K - based ... Partner closely with engineering, product, and executive leadership to define technical strategy ...

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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 Aug 21, 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.

What are the most commonly searched types of Machine Learning Compiler Engineer jobs in San Francisco, CA?

The most popular types of Machine Learning Compiler Engineer jobs in San Francisco, CA are:

What are popular job titles related to Remote Machine Learning Compiler Engineer jobs in San Francisco, CA?

For Remote Machine Learning Compiler Engineer jobs in San Francisco, CA, the most frequently searched job titles are:

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Remote Machine Learning Compiler Engineer jobs near you

Machine Learning Engineer

Elicit

Oakland, CA • On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 21 days ago

Job description

About Elicit
Elicit is building the reasoning layer for science and decision-making. We use language models to search over 125 million papers, extract data, and surface insights so that researchers, policy-makers, and industry leaders can go from questions to evidence-backed decisions in minutes.
Today, hundreds of thousands of researchers have used Elicit to speed up literature reviews, automate systematic reviews, and explore new domains. As we expand our impact beyond academic research, we are laying the groundwork for ML systems that are systematic, transparent, and unbounded when reasoning at scale.
To do this, Elicit is pioneering supervision of process, not outcomes. Instead of favoring large black-box models, we break complex questions down into human-legible steps and supervise the reasoning process itself. This approach delivers more transparent, defensible answers today and charts a safer path toward advanced AI tomorrow.
Our vision is ambitious: we're building the default starting point for understanding and reasoning through any hard question. We invite you to help us build that future.
(See how people use Elicit today on Twitter; explore our vision in the roadmap.)
About the role
As a Machine Learning Engineer at Elicit, you'll build products and workflows that help researchers and scientific teams make higher quality decisions with language models.
This is not a role for someone who only wants to develop models in isolation from user impact. A large part of the work is software engineering: building product experiences, APIs, data integrations, evaluation systems, and reliable harnesses that make language models reliably useful and trustworthy in high-stakes domains.
You'll work on problems like:
  • Turning messy, ambiguous research tasks into clear product experiences
  • Building interfaces and artifacts that help users understand, trust, and act on model outputs, thinking beyond the chat interface while leveraging full model capabilities
  • Combining language models with external tools, structured and unstructured data, and retrieval systems
  • Improving quality through building careful evaluations, truth-conducive model environments and tools, and targeted ML modeling where the impact is high

What you'll build
  • Agentic harnesses for target assessment, evidence synthesis, and experiment planning that allow models to provide guarantees about their processes
  • Data integrations across literature, scientific databases, customer data, and internal tools
  • APIs that customers can use in their own systems
  • Evaluation systems that help us understand whether a change actually improves user outcomes
  • Trust and transparency features, like source-quality signals, intermediate reasoning, and better ways to inspect and fix outputs
Example projects
Examples of projects you could work on:
  • Build a target-assessment workflow that combines literature, genetics, chemistry, clinical, regulatory, and company data into a shareable artifact.
  • Build experiment-planning and iteration tools that help researchers decide what to do next and learn from new results.
  • Build evidence-monitoring workflows that keep teams up to date through alerts, briefs, and living reports.
  • Build enterprise APIs and structured-output pipelines that plug Elicit into customers' internal systems.
  • Build interfaces that make it easier to inspect, trust, and correct model outputs.
  • Build workflow-specific evals and quality systems that tell us whether a product change actually helped users.
  • Improve extraction, reasoning, or search quality with better prompts, better system design, or finetuning when appropriate.

What you bring
  • A strong software engineering background and can build end-to-end systems, not just scripts or notebooks
  • Fluency with language models to reason well about prompting, retrieval, evals, failure modes, and where (and how) finetuning is or isn't worth it
  • Strong product sense and likes turning fuzzy user problems into concrete things people can use
  • An excitement to solve difficult, creative problems rather than narrow optimization on well-defined benchmarks
  • Ability to move across backend, data, and model layers as needed
  • Clear communication with product, design, domain experts, and other engineers
  • Ability to use coding assistants effectively and thoughtfully, and has adapted their workflow to become much more effective with them

To get a sense for how some of us look at applications, see this thread. (The short version: Wherever we can, we prefer to directly evaluate work.)
You'll thrive here if you:
  • Like shipping user-facing things quickly
  • Enjoy working on ambiguous problems with a lot of autonomy
  • Care about product quality and user trust, not just technical novelty
  • Want to build new kinds of software made possible by language models
  • Are excited to use AI tools as part of your daily engineering workflow, while still applying strong judgment
What we're not looking for:
This is probably not the right role if you mainly want to:
  • do low-level model systems work like CUDA optimization or model serving infrastructure as your primary focus
  • work only on research experiments without owning production systems
  • optimize benchmark numbers without much connection to user workflows or product outcomes

We do care about model quality, evals, and sometimes finetuning. But those matter because they help us build products users can rely on, not as ends in themselves.
Am I a good fit?
Consider these questions:
  1. How does a transformer work?
  2. What is a tokenizer?
  3. What is a decorator in Python?
  4. What are generic types?

Strong applicants will find it easy to answer these questions.
Location and travel
We have a great office in Oakland, CA, and we'd love to see you there if you're local. That said, we're just as happy for you to work remotely. We do get the whole team together for a quarterly retreat somewhere fun, because in-person time matters to us.
Benefits
In addition to working on important problems as part of a happy, productive, and positive team, we also offer great benefits (with some variation based on work location):
  • Flexible work environment - work from our office in Oakland or remotely as long as you can travel to work in-person for retreats and coworking events
  • Fully covered health, dental, vision, and life insurance for you, generous coverage for the rest of your family
  • Flexible vacation policy, with a minimum recommendation of 20 days/year + company holidays
  • 401K with a 6% employer match
  • Every Elician receives a $200 monthly wellbeing stipend to spend on whatever supports your health and wellbeing.
  • A new Mac + $1,000 budget to set up your workstation or home office in your first year, then $500 every year thereafter
  • $1,000 quarterly AI Experimentation & Learning budget, so you can freely experiment with new AI tools to incorporate into your workflow, take courses, purchase educational resources, or attend AI-focused conferences and events
  • A team administrative assistant that you can delegate personal and work tasks to
  • Commuter benefits, a relocation bonus, and more!
  • You can find more reasons to work with us in this thread.

Compensation
For all roles at Elicit, we use a data-backed compensation framework to make sure our salaries are market-competitive, equitable, and simple. For this role, we're targeting starting ranges of:
  • Career (L3): $185-220K + equity
  • Senior (L4): $220-260K + equity
  • Expert/Staff (L5): $250-320K + significant equity

We're optimizing for a hire who can contribute at a L4/senior-level or above. We'd love to meet staff/principal level contributors as well.
We also offer above-market equity for all roles at Elicit, as well as employee-friendly equity terms.
Join us!

Apply