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Embedded Machine Learning Engineer Jobs in Pittsburg, 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 ...

Hive

Senior Machine Learning Engineer

San Francisco, CA · On-site

$160K - $250K/yr

Senior Machine Learning Engineer In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at ...

Hive

Senior Machine Learning Engineer

San Francisco, CA · On-site

$160K - $250K/yr

Senior Machine Learning Engineer In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at ...

Hive

Staff Machine Learning Engineer

San Francisco, CA · On-site

$200K - $300K/yr

Staff Machine Learning Engineer In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at ...

Hive

Staff Machine Learning Engineer

San Francisco, CA · On-site

$200K - $300K/yr

Staff Machine Learning Engineer In order to execute our vision, we need to grow our team of best-in-class machine learning engineers. We are looking for developers who are excited about staying at ...

Reddit

Machine Learning Engineer

San Francisco, CA · On-site

$187K - $260K/yr

Special Skill Requirements: 1.) Machine Learning; 2.) TensorFlow; 3.) Python and SQL; 4.) Feature Engineering and Selection; 5.) Ads predictive model design; 6.) Ads predictive model offline training ...

Onyx

Machine Learning Engineer

San Francisco, CA · On-site

$150K - $250K/yr

Have 3+ years of AI/ML engineering experience building real-world applications * have in-depth experience with PyTorch/Tensorflow, NLP models, and standard ML algorithms * Are up date with new ...

Warnerbros

Senior Machine Learning Engineer

San Francisco, CA · On-site

$123K - $169K/yr

Senior Machine Learning Engineer Team: Data & Audience Platform (DAP) - ML Engineering What We Do Warner Bros. Discovery (WBD) is home to the world's most iconic entertainment, news, and sports ...

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All JobsEmbedded Machine Learning Engineer JobsEmbedded Machine Learning Engineer Jobs in Pittsburg, CA

Embedded Machine Learning Engineer information

See Pittsburg, CA salary details

$77.8K

$170.5K

$193.4K

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

As of Jul 6, 2026, the average yearly pay for embedded machine learning engineer in Pittsburg, CA is $170,491.00, according to ZipRecruiter salary data. Most workers in this role earn between $146,200.00 and $192,300.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Embedded Machine Learning Engineer, and why are they important?

To thrive as an Embedded Machine Learning Engineer, you need expertise in machine learning algorithms, embedded systems programming (C/C++ or Python), and a solid understanding of hardware constraints, usually supported by a degree in computer science, electrical engineering, or related fields. Familiarity with tools like TensorFlow Lite, ONNX, microcontroller SDKs, and experience with real-time operating systems (RTOS) are typically required. Strong problem-solving, communication skills, and the ability to collaborate across multidisciplinary teams help you stand out in this role. These skills are crucial for efficiently deploying intelligent models on resource-constrained devices, ensuring optimal performance and seamless integration in real-world applications.

What does an Embedded Machine Learning Engineer do?

An Embedded Machine Learning Engineer designs and implements machine learning models that can run efficiently on embedded systems, such as microcontrollers and edge devices. Their work involves optimizing algorithms to fit within the resource constraints of these devices, integrating ML models into hardware, and ensuring real-time performance. They collaborate closely with hardware engineers and software developers to deploy intelligent features in products like smart sensors, IoT devices, and autonomous systems.

What are some common challenges faced by Embedded Machine Learning Engineers when deploying models to hardware devices?

One of the main challenges for Embedded Machine Learning Engineers is optimizing machine learning models to run efficiently on devices with limited memory, processing power, and energy capacity. Ensuring real-time performance while maintaining accuracy often requires model quantization, pruning, or using lightweight architectures. Additionally, engineers must carefully manage hardware-software integration and address issues like compatibility with various microcontrollers and ensuring secure, reliable updates for deployed models. Close collaboration with hardware engineers and software developers is essential to overcome these challenges and deliver robust embedded AI solutions.

What is the difference between Embedded Machine Learning Engineer vs Firmware Engineer?

AspectEmbedded Machine Learning EngineerFirmware Engineer
Required CredentialsBachelor's/Master's in Computer Science, Electrical Engineering, or related; knowledge of ML frameworksBachelor's in Electrical Engineering, Computer Engineering, or related; embedded systems experience
Work EnvironmentDevelops ML models for embedded devices, often in IoT or smart devicesDesigns and implements low-level firmware for hardware devices
Industry UsageTech companies, IoT, consumer electronics, automotiveConsumer electronics, automotive, industrial equipment

The Embedded Machine Learning Engineer focuses on integrating machine learning models into embedded systems, while the Firmware Engineer specializes in developing low-level software for hardware devices. Both roles require embedded systems knowledge but differ in their core focus and skill sets.

What are popular job titles related to Embedded Machine Learning Engineer jobs in Pittsburg, CA? For Embedded Machine Learning Engineer jobs in Pittsburg, CA, the most frequently searched job titles are:
What cities near Pittsburg, CA are hiring for Embedded Machine Learning Engineer jobs? Cities near Pittsburg, CA with the most Embedded Machine Learning Engineer job openings:
Embedded 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 5 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