ZipRecruiter

Log In

1

Embedded Machine Learning Engineer Jobs in Pittsburg, CA

Atoms

Staff Machine Learning Engineer

San Francisco, CA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

What we're seeking A visionary Machine Learning Engineer to join our founding team who will help ... Hands-on experience optimizing models for edge deployment or custom embedded GPU targets. * Deep ...

ATOMS Careers page

Senior Machine Learning Engineer

San Francisco, CA · On-site

$144K - $190K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

What we're seeking A visionary Machine Learning Engineer to join our founding team who will help ... Hands-on experience optimizing models for edge deployment or custom embedded GPU targets. * Deep ...

Kikoff

Senior Machine Learning Engineer

San Francisco, CA

$123K - $169K/yr

We are seeking a Senior Machine Learning Engineer to join our team. This role will focus on developing and maintaining machine learning infrastructure and operations, particularly for our cash ...

Tensec

Machine Learning Engineer

San Francisco, CA · On-site

$100 - $130/hr

  • Medical

  • Dental

  • Vision

We are looking for a Machine Learning Engineer to build creative, practical, and robust solutions ... Experience deploying & optimizing ML models to run fast on embedded compute like NVIDIA Jetson

Orchard

Machine Learning Engineer

San Francisco, CA · On-site

$120 - $190/hr

  • Medical

  • Dental

  • Vision

We are looking for a Machine Learning Engineer to build creative, practical, and robust solutions ... Experience deploying & optimizing ML models to run fast on embedded compute like NVIDIA Jetson

Orchard Robotics

Machine Learning Engineer

San Francisco, CA · On-site

$135K - $210K/yr

  • Medical

  • Dental

  • Vision

We are looking for a Machine Learning Engineer to build creative, practical, and robust solutions ... Experience deploying & optimizing ML models to run fast on embedded compute like NVIDIA Jetson

Workday

Machine Learning Engineer III

Pleasanton, CA · On-site

About the Role As a Machine Learning Engineer on the AI Core team, you will develop tailored user experiences using advanced Agentic AI, LLMs and RAG. You will collaborate with other engineers to ...

Kikoff

Senior Machine Learning Engineer

San Francisco, CA · On-site

$123K - $169K/yr

We are seeking a Senior Machine Learning Engineer to join our team. This role will focus on developing and maintaining machine learning infrastructure and operations, particularly for our cash ...

Elicit

Machine Learning Engineer

Oakland, CA · On-site +1

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

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 ...

Handshake

Machine Learning Engineer I, Network

San Francisco, CA · On-site

$151K - $189K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

About the Role Handshake is hiring a Machine Learning Engineer for the Network and Handshake AI Marketplace Relevance team. AI is transforming how students navigate their careers, and we're committed ...

Apply

Machine Learning Engineer I, Network

San Francisco, CA · On-site

$140 - $190/hr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

About the Role Handshake is hiring a Machine Learning Engineer for the Network and Handshake AI Marketplace Relevance team. AI is transforming how students navigate their careers, and we're committed ...

previous page

Showing results 41-60

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 Aug 16, 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?

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 job categories do people searching Embedded Machine Learning Engineer jobs in Pittsburg, CA look for?

The top searched job categories for Embedded Machine Learning Engineer jobs in Pittsburg, CA 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
Infographic showing various Embedded Machine Learning Engineer job openings in Pittsburg, CA as of July 2026, with employment types broken down into 1% As Needed, 65% Full Time, 31% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $170,491 per year, or $82 per hour.

Staff Machine Learning Engineer

Atoms

San Francisco, CA • On-site

Full-time

Medical, Dental, Vision, Life, Retirement

Re-posted 20 days ago

Job description

Who we are
Atoms is building the machines that power the next era of progress.
Over the last decade, software has transformed the digital world. But the physical world, where food is made, minerals are mined, goods are moved, and industries are run, remains far less intelligent, far less efficient, and far more constrained. We're changing that.
Atoms builds Physical AI- real-world robots for the industries that move civilization forward, starting with food, mining, and transport. Our systems are designed to understand, predict, and control the real world with precision, turning complex physical operations into something more reliable, more scalable, and more productive.
This work requires more than robotics. It requires deep integration across hardware, software, AI, operations, manufacturing, and real estate. We don't just build machines in a lab. We deploy them into real environments, operate them, learn from them, and improve them until they work at scale.
We are roboticists, engineers, operators, and builders. We believe the next great technology companies will not only transform information, but the physical systems that shape everyday life.
If you want to work on hard problems with real-world impact, join us.
What we're seeking
A visionary Machine Learning Engineer to join our founding team who will help bridge the gap between high-level AI research and real-world physical actuation for our next-generation autonomous transport platforms. We are actively hiring across three core specialized subcategories: AI Research, Post-Training Optimization, and Data Engineering.
AI Researcher (World Models & VLA)
What you'll do
  • Research and develop cutting edge RL and distillation techniques for trajectory planning
  • Integrate emerging research from the broader AI community, identifying and prototyping the most promising solutions
  • Design and deploy end-to-end multimodal models that translate real-time visual perception and high-level behavioral goals into physical vehicle actuation
  • Develop interactive world models from raw multi-sensor logs, allowing the team to re-simulate events and query what a vehicle would see if it altered its trajectory
  • Ensure core autonomous driving models can seamlessly adapt to novel urban environments and edge cases
  • Partner with validation and QA teams to run model releases through rigorous simulated scenarios, detecting regressions and identifying systemic performance bottlenecks.
What we're looking for
  • 10+ years of non-internship professional MLE experience.
  • Deep expertise in applying AI Transformers to robotics, physical actuation, or spatial-temporal data.
  • Proven track record designing or training multimodal systems, large-scale VLA models, or generative Diffusion models.
  • Strong background in Sensor Fusion, combining inputs from Cameras, LiDAR, and Radar.
  • Fluency in PyTorch or JAX for training large-scale models.
  • Experience with multi-task learning, Birds-Eye-View (BEV) frameworks, representation learning, or data tokenization is highly preferred.
  • Proficiency in Python and familiarity with C++.
Post-Training & Optimization
What you'll do
  • Own the post-training lifecycle by distilling, quantizing, and optimizing massive models to run with low latency on vehicle edge hardware.
  • Profile real-time inference pipelines to identify and eliminate CPU, GPU, and memory bandwidth bottlenecks on the vehicle.
  • Work with low-level hardware, electrical, and firmware teams to iterate on custom carrier boards, sensor interfaces, and GPUs on edge devices.
  • Benchmark and deploy models utilizing hardware-accelerated runtimes (e.g., TensorRT, CUDA) to minimize inference times under strict constraints.
What we're looking for
  • 10+ years of non-internship professional MLE experience.
  • Strong background in machine learning engineering with a focus on model optimization, distillation, and deployment.
  • Hands-on experience optimizing models for edge deployment or custom embedded GPU targets.
  • Deep understanding of profiling tools and debugging resource constraints across CPU/GPU boundaries.
  • Experience with modern deep learning frameworks (PyTorch or JAX) and runtime compilation.
  • Robust programming skills in Python and C++.
  • Familiarity with low-level camera/sensor interfaces and robotics hardware is a significant plus.
Data & Long-Tail Scenarios
What you'll do
  • Architect automated pipelines to ingest, filter, and identify rare, high-value, and long-tail scenarios out of multi-petabyte multi-sensor datasets.
  • Target and extract complex structural corner cases from real-world driving logs to continuously feed, challenge, and improve our end-to-end behavior models.
  • Iterate closely with QA, testing, and simulation teams to transform ambiguous real-world anomalies into concrete data blocks for simulation testing.
  • Implement programmatic data curation, active learning strategies, and statistical quality metrics to optimize the signal-to-noise ratio of our training pipelines.
What we're looking for
  • 10+ years of non-internship professional MLE experience.
  • Professional experience building data curation pipelines, active learning workflows, or data mining architectures for massive physical datasets.
  • Strong familiarity with robotics data structures and spatial frameworks, including Birds-Eye-View (BEV) or spatial tokenization.
  • Experience processing and structuring raw data from Cameras, LiDAR, and Radar.
  • Expert-level proficiency in Python, data engineering frameworks, and PyTorch/JAX.
  • Exceptional ability to navigate, structure, and derive signal from highly ambiguous, messy, or undefined real-world data distributions.

Why join us
At Atoms, you'll work on one of the defining challenges of our time - bringing automation into the physical world to drive real, lasting impact. We exist to uncover valuable unknown truths and turn them into progress, which means constantly pushing beyond what's known and building what doesn't yet exist. The work is ambitious and often challenging, but it's grounded in a shared sense of purpose and a team committed to seeing it through together. Our work only matters if it serves others, and we know that meaningful progress depends on the trust of the people we serve and the strength of our team-so we invest in both, creating an environment where you can do your best work and grow.
What else you need to know
This role is based in our San Francisco office. Atoms is a company driven by invention and continuous change - we are constantly reimagining our industries, building new products, and refining how we operate. We do our best work together. That's why all of our office-based teams work onsite, five days a week.
The base salary range for this role is$273,000 - $345,000 per year.
Actual compensation will be determined on an individual basis and may vary depending on experience, skills, and qualifications.
Base salary is just one part of your total rewards package. You may also be eligible for equity awards and an annual performance-based bonus.
Benefits Summary (USA Full-Time Exempt Employees):
  • Medical, Dental, Vision, Disability, and Life Insurance
  • Flexible Spending Account / Health Savings Account Options
  • 401(k)
  • Equity
  • Sick Time, Unlimited Flexible Time Off, and Paid Holidays
  • Paid Parental Leave
  • Pre-Tax Commuter Benefit Plan
  • Team lunch in our SoMa office every Tuesday and Thursday

Benefits are subject to change at the company's discretion.
Atoms accepts applications on an ongoing basis.

Apply