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Remote Full Stack Machine Learning Engineer Jobs in California

Elicit

Machine Learning Engineer

Oakland, CA · On-site +1

... full model capabilities * Combining language models with external tools, structured and ... Location and travel We have a lovely office in Oakland, CA, but we also have remote employees ...

Roblox

Senior Machine Learning Engineer, Economy

San Mateo, CA · On-site +1

$119K - $163.40K/yr

Own the full ML lifecycle for your projects: data exploration, feature engineering, model design ... Contribute to the technical direction of the Economy ML team: help refine our modeling stack ...

Twilio

Machine Learning Engineer

San Francisco, CA · Remote

Our dedication to remote-first work, and strong culture of connection and global inclusion means ... So, if you're ready to unleash your full potential, do your best work, and be the best version of ...

Motional

Senior Full Stack Engineer

San Francisco, CA · On-site +1

$149K - $198.50K/yr

... for machine learning (ML) engineers and improving the performance and adaptability of our ... be fully remote. The salary range for this role is an estimate based on a wide range of ...

Care.coach

Lead Machine Learning Engineer

Millbrae, CA · On-site +1

$119K - $156.70K/yr

... machine learning applications for practical use * 2+ years in an agile software development ... LAMP stack w/ PHP, and Django web framework * Academic publications, conference speaker roles, or ...

DocuSign

Senior Machine Learning Engineer

San Francisco, CA · On-site +1

$186.10K - $300.55K/yr

Employee divides their time between in-office and remote work. Access to an office location is ... Full Health Benefits Plans: options for 100% employer paid and minimum employee contribution health ...

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Remote Full Stack Machine Learning Engineer information

What are the key skills and qualifications needed to thrive as a Remote Full Stack Machine Learning Engineer, and why are they important?

To thrive as a Remote Full Stack Machine Learning Engineer, you need proficiency in programming languages (such as Python or JavaScript), a solid understanding of machine learning algorithms, experience with web development frameworks, and typically a degree in computer science or a related field. Familiarity with tools like TensorFlow, PyTorch, Docker, cloud computing platforms (AWS, GCP), and version control systems (Git) is essential. Strong problem-solving skills, self-motivation, and clear communication are crucial soft skills, especially in remote and cross-functional team environments. These combined skills ensure effective design, deployment, and integration of machine learning solutions in scalable web applications while maintaining productivity in a remote setting.

What are some common challenges faced by remote Full Stack Machine Learning Engineers, and how can they be addressed?

Remote Full Stack Machine Learning Engineers often encounter challenges such as managing effective collaboration with cross-functional teams and ensuring smooth deployment of machine learning models into production environments. To address these, it's important to establish clear communication channels, regularly participate in virtual stand-ups, and use collaborative platforms such as GitHub and Slack. Additionally, staying organized with version control and thorough documentation helps maintain project transparency and ensures seamless handoffs between backend and frontend development. Proactively seeking feedback and scheduling regular check-ins with team members can further enhance productivity and integration within the team.

What is a Remote Full Stack Machine Learning Engineer?

A Remote Full Stack Machine Learning Engineer is a professional who designs, develops, and deploys machine learning solutions while working remotely. They handle both the front-end and back-end aspects of machine learning projects, including data preprocessing, model building, API development, and integration with user interfaces or cloud platforms. This role requires expertise in programming, machine learning frameworks, cloud services, and web technologies, allowing them to build end-to-end AI-driven applications from anywhere in the world.

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

AspectRemote Full Stack Machine Learning EngineerRemote Data Scientist
Primary FocusDeveloping end-to-end machine learning applications, including backend, frontend, and model deploymentAnalyzing data, creating models, and generating insights without necessarily building full applications
Skills RequiredProgramming (Python, JavaScript), ML frameworks, web development, deployment toolsStatistics, data analysis, visualization, Python/R, SQL
Work EnvironmentCollaborates with developers, data engineers, and product teams in tech-driven companiesWorks with data teams, analysts, and business units in various industries

While both roles involve working with data and machine learning, a Remote Full Stack Machine Learning Engineer builds complete applications with integrated ML models, whereas a Remote Data Scientist focuses on data analysis and model creation without necessarily developing full applications.

What are the most commonly searched types of Full Stack Machine Learning Engineer jobs in California? The most popular types of Full Stack Machine Learning Engineer jobs in California are:
What are popular job titles related to Remote Full Stack Machine Learning Engineer jobs in California? For Remote Full Stack Machine Learning Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Remote Full Stack Machine Learning Engineer jobs in California look for? The top searched job categories for Remote Full Stack Machine Learning Engineer jobs in California are:
What cities in California are hiring for Remote Full Stack Machine Learning Engineer jobs? Cities in California with the most Remote Full Stack Machine Learning Engineer job openings:
Remote Full Stack 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 27 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!

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