ZipRecruiter

Log In

2

Remote Machine Learning Engineer Jobs in Sunnyvale, CA

Elicit

Machine Learning Engineer

Oakland, CA · On-site +1

... role As a Machine Learning Engineer at Elicit, you'll build products and workflows that help ... Location and travel We have a lovely office in Oakland, CA, but we also have remote employees ...

BMW

Machine Learning Engineer

Mountain View, CA · On-site +1

$162K - $224K/yr

Proficiency in programming languages relevant to AI, Machine Learning and Application Development ... remote work capability (hybrid). WHAT YOU CAN LOOK FORWARD TO. * Medical, Dental, and Vision ...

RADAR

Machine Learning Engineer

Sunnyvale, CA · On-site +1

$120K - $190K/yr

San Diego, CA or Sunnyvale, CA or Remote ABOUT US At RADAR, we're transforming the way the world ... ABOUT THE JOB We are looking for a Machine Learning Engineer to help build and develop our ML ...

Bill.com

Senior Machine Learning Engineer

San Jose, CA · On-site +1

$161K - $194K/yr

Whether in one of our offices in San Jose, CA, Draper, UT, or in a remote-eligible role, BILLders ... As a Senior Machine Learning Engineer , you'll play a pivotal role in designing, building, and ...

next page

Showing results 1-20

All JobsRemote Machine Learning Engineer JobsRemote Machine Learning Engineer Jobs in Sunnyvale, CA

Remote Machine Learning Engineer information

See Sunnyvale, CA salary details

$36.8K

$150.3K

$225.8K

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

As of Jun 17, 2026, the average yearly pay for remote machine learning engineer in Sunnyvale, CA is $150,293.00, according to ZipRecruiter salary data. Most workers in this role earn between $118,500.00 and $180,900.00 per year, depending on experience, location, and employer.

What are some typical challenges faced by Remote Machine Learning Engineers, and how are they addressed?

Remote Machine Learning Engineers often face challenges such as coordinating across different time zones, ensuring smooth communication with team members, and accessing large datasets or secure environments remotely. Organizations commonly address these by using robust collaboration tools (like Slack, GitHub, and Jira), establishing clear documentation, and setting regular virtual meetings to maintain alignment. Many companies also provide secure remote environments or VPN access for handling sensitive data and code. Proactive communication and organized workflows help mitigate these challenges, enabling engineers to remain productive and connected to their teams.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $500,000 or more annually, especially in high-cost-of-living areas or within top tech companies. Achieving this level often requires a strong track record, specialized certifications, and sometimes equity or bonuses as part of compensation packages.

Which 5 jobs will survive AI?

Remote Machine Learning Engineers are likely to continue to be in demand as AI advances, since they develop and maintain AI models and systems. Jobs that require complex problem-solving, creativity, and emotional intelligence, such as healthcare professionals, educators, and skilled trades, are also expected to persist. Additionally, roles involving oversight, ethical considerations, and human interaction will remain essential despite automation.

What are the key skills and qualifications needed to thrive in the Remote Machine Learning Engineer position, and why are they important?

To thrive as a Remote Machine Learning Engineer, you need a strong background in computer science, mathematics, and experience with machine learning algorithms, typically supported by a relevant degree and prior project work. Proficiency with programming languages like Python, machine learning frameworks such as TensorFlow or PyTorch, and familiarity with cloud computing platforms is crucial, and certifications like AWS Certified Machine Learning can enhance your profile. Excellent communication, self-motivation, and time-management skills are also essential for collaborating across remote teams and meeting project goals. These combined technical and soft skills are vital for developing effective machine learning solutions while ensuring productivity and collaboration in a virtual work environment.

What is a Remote Machine Learning Engineer job?

A Remote Machine Learning Engineer designs, develops, and deploys machine learning models while working from a remote location. They preprocess data, train and optimize models, and integrate them into production systems. Their role often involves collaborating with data scientists, software engineers, and stakeholders to solve complex problems using AI. Strong programming skills in Python, experience with ML frameworks like TensorFlow or PyTorch, and cloud computing knowledge are essential. Remote ML engineers must also communicate effectively and manage their time efficiently to work asynchronously with teams.

Can ML engineers work remotely?

Yes, many machine learning engineers work remotely, especially in roles that involve programming, data analysis, and model development using tools like Python, TensorFlow, and cloud platforms. Remote work arrangements depend on the employer's policies and project requirements, but it is common in the tech industry for ML engineers to work from home or other locations.

Is ML full of coding?

A remote machine learning engineer role typically involves significant coding, especially in languages like Python or R, to develop algorithms and models. However, it also requires understanding data, model evaluation, and sometimes deploying solutions, making coding a core but not the sole component of the job.
What are the most commonly searched types of Machine Learning Engineer jobs in Sunnyvale, CA? The most popular types of Machine Learning Engineer jobs in Sunnyvale, CA are:
What are popular job titles related to Remote Machine Learning Engineer jobs in Sunnyvale, CA? For Remote Machine Learning Engineer jobs in Sunnyvale, CA, the most frequently searched job titles are:
What job categories do people searching Remote Machine Learning Engineer jobs in Sunnyvale, CA look for? The top searched job categories for Remote Machine Learning Engineer jobs in Sunnyvale, CA are:
What cities near Sunnyvale, CA are hiring for Remote Machine Learning Engineer jobs? Cities near Sunnyvale, CA with the most Remote Machine Learning Engineer job openings:
Remote Machine Learning Engineer jobs near you
Machine Learning Engineer

Machine Learning Engineer

Elicit

Oakland, CA • On-site, Remote

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 15 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 lovely office in Oakland, CA, but we also have remote employees across the US. It's important to us to spend time with our teammates, so we ask that all Elicians come together for a quarterly team retreat, normally in or around the SF bay area.
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
  • 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): $185-230K + equity
  • Senior (L4): $230-260K + equity
  • Expert/Staff (L5): $255-340K + 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