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Embedded Machine Learning Engineer Jobs in Pasadena, CA

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

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Embedded Machine Learning Engineer information

See Pasadena, CA salary details

$76.4K

$167.3K

$189.8K

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

As of Sep 6, 2026, the average yearly pay for embedded machine learning engineer in Pasadena, CA is $167,311.00, according to ZipRecruiter salary data. Most workers in this role earn between $143,400.00 and $188,700.00 per year, depending on experience, location, and employer.

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 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 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 Pasadena, CA?

For Embedded Machine Learning Engineer jobs in Pasadena, CA, the most frequently searched job titles are:

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

The top searched job categories for Embedded Machine Learning Engineer jobs in Pasadena, CA are:

What cities near Pasadena, CA are hiring for Embedded Machine Learning Engineer jobs?

Cities near Pasadena, CA with the most Embedded Machine Learning Engineer job openings:

Infographic showing various Embedded Machine Learning Engineer job openings in Pasadena, CA as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $167,311 per year, or $80.4 per hour.

Machine Learning Engineer - Vision

Hadrian Automation

Torrance, CA โ€ข On-site

$160K - $300K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 17 days ago


Job description

Hadrian - Manufacturing the Future

Hadrian is building autonomous factories to reindustrialize America. By combining AI, advanced software, robotics, and full-stack manufacturing, we help aerospace and defense companies build rockets, satellites, aircraft, ships, and other mission-critical systems up to 10x faster and at significantly lower cost.


Following our $1.37B Series D at a $7.87B valuation, Hadrian is rapidly expanding our manufacturing footprint, launching new capabilities across welding, casting, forging, electronics, additive manufacturing, and more, while scaling our Factory-as-a-Service platform to transform how critical products are built.


Backed by leading investors including JPMorgan Chase, Valor Equity Partners, Andreessen Horowitz, Founders Fund, 137 Ventures, Lux Capital, T. Rowe Price, and Morgan Stanley, we’re building the future of American manufacturing—and looking for exceptional people to help make it happen.


If you’re ready to take on the most challenging and rewarding work of your career while helping create American manufacturing jobs for generations to come, you’re exactly who we’re looking for.

The Role

Copilot is our system for automating Design for Manufacturing (DFM) analysis and generating manufacturing processes. We work directly with some of the best operators in the world to identify high-impact opportunities to automate and augment with software.

Our team owns problems end-to-end: we design the software, define the manufacturing processes, and ensure they can be executed reliably in our factories. The work spans computational geometry, CAD/CAM integrations, high-performance systems, and full-stack web tooling. We execute whatever is required to deliver a working solution and best serve our users.

The DFM team within Copilot is building the manufacturing data intelligence layer that serves as the tip of the spear for our automation stack. This platform ingests, interprets, and reasons over the full spectrum of manufacturing data (mechanical drawings, quality documentation, CAD data) and transforms it into structured, actionable information for the factory.

As a Senior Machine Learning Engineer, you will own the ML lifecycle for the detection and segmentation models at the core of our manufacturing intelligence pipeline.

What You'll Do
  • Research, develop, and deploy cutting-edge object detection and segmentation models for layout analysis, document understanding, and semantic part understanding

  • Work alongside the core engineering team to build and maintain annotation tooling, implement active learning loops, and engineer synthetic data augmentation strategies

  • Develop evaluation frameworks to capture metrics that extend beyond mAP/IoU, precisely quantifying system behavior and user impact

  • Collaborate with the other members of the machine learning team to set the technical and product roadmaps for the AI platform

  • Burn down the long tail, as every percentage point of accuracy maps to man-years of time savings at our scale

What We're Looking For
  • 5-8 years of professional experience building and shipping computer vision models, with an emphasis on detection and/or segmentation

  • Deep expertise in modern architectures using transformer backbones (ViT, Swin, etc…)

  • Strong Python and PyTorch fluency: You've written custom training loops, loss functions, and data loaders from scratch when needed

  • Production deployment ownership: You've shipped models to production and have been responsible for endpoint and model health

  • MS or PhD in Computer Science, Electrical Engineering, or related field preferred; equivalent industry experience valued equally

Bonus Points
  • You have a passion for manufacturing and believe that the industry needs better software

  • Previously worked in aerospace, defense, or manufacturing, and have experience working with manufacturing data

  • Published research, achieved SOA results on relevant benchmarks, or contribute to open-source frameworks

  • Prior experience working in a high-ownership startup environment

Compensation

For this role, the target salary range is $160,000 - $300,000 (actual range may vary based on experience).

This is the lowest to highest salary we reasonably and in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future. An employee's pay position within the salary range will be based on several factors, including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs.

Benefits for Full-time Employees
  • Medical, dental, vision, and life insurance plans for employees

  • 401k

  • Relocation support may be provided for certain situations, based on business need.

  • Flexible vacation policy

  • Equity

ITAR Requirements

To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here.

Use of AI in hiring

Hadrian uses AI-assisted tools in our recruiting and hiring processes to help our team work more efficiently. This may include tools that help organize and analyze recruiting data, as well as an AI-powered notetaker that can record and transcribe interviews and help coordinate feedback. These tools support our team and are not used to make hiring decisions. All candidate evaluations and hiring decisions are performed by humans. If an interview will be recorded, you will be notified in advance and may opt out at any time with no impact on your candidacy. Candidate data processed through these tools is subject to the same protections described in our Privacy Policy.

Hadrian Is An Equal Opportunity Employer

It is the Company’s policy to provide equal employment opportunity for all applicants and employees. The Company does not unlawfully discriminate on the basis of race inclusive of traits historically associated with race (including, but not limited to, hair texture and protective hairstyles, such as braids, locks and twists), color, religion, sex (including pregnancy, childbirth, or related medical conditions), gender identity, gender expression, transgender status, national origin (including, in California, possession of a drivers license), ancestry, citizenship, age, physical or mental disability, height or weight, medical condition, family care status, military or veteran status, marital status, domestic partner status, sexual orientation, genetic information, exercise of reproductive rights, any other basis protected by local, state, or federal laws, or any combination of the above characteristics. When necessary, the Company also makes reasonable accommodations for disabled candidates and employees, including for candidates or employees who are disabled by pregnancy, childbirth, or related medical conditions.