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Embedded Machine Learning Engineer Jobs (NOW HIRING)

Familiarity with embedded machine learning, real-time systems, or deploying machine learning on ... D. in Electrical Engineering, Computer Science, or a related field. * Minimum of 3 years of ...

Machine Learning Engineer

San Diego, CA · On-site

$122.80K - $184.20K/yr

... of experience in embedded system development and optimization with application to a specific ... programming language suitable for machine learning (e.g., Python, R, C, C++) • 1+ year of ...

Overview Machine Learning Engineer, AI Platform As a Machine Learning Engineer, you will design ... Experience working directly with non-technical stakeholders or in embedded/consulting-style ...

Overview Machine Learning Engineer, AI Platform As a Machine Learning Engineer, you will design ... Experience working directly with non-technical stakeholders or in embedded/consulting-style ...

We are looking for a Machine Learning Engineer to help us create artificial intelligence products. Machine Learning Engineer responsibilities include creating machine learning models and retraining ...

We are seeking a Machine Learning Engineer to join our team at MORSE. You will play a pivotal role ... embedded system. You will be part of our team working to accelerate our US National Security ...

Machine Learning Engineer Location: Fort Meade, MD Required Clearance : TS/SCI w/ Full-Scope Poly Salary: Competitive We are seeking a highly skilled and motivated Machine Learning Engineer to join ...

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Embedded Machine Learning Engineer information

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$70K

$153.4K

$174K

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

As of May 30, 2026, the average yearly pay for embedded machine learning engineer in the United States is $153,383.00, according to ZipRecruiter salary data. Most workers in this role earn between $131,500.00 and $173,000.00 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 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 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 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.

More about Embedded Machine Learning Engineer jobs
What cities are hiring for Embedded Machine Learning Engineer jobs? Cities with the most Embedded Machine Learning Engineer job openings:
What states have the most Embedded Machine Learning Engineer jobs? States with the most job openings for Embedded Machine Learning Engineer jobs include:
Infographic showing various Embedded Machine Learning Engineer job openings in the United States as of May 2026, with employment types broken down into 98% Full Time, 1% Part Time, and 1% Contract. Highlights an 92% Physical, and 8% Remote job distribution, with an average salary of $153,383 per year, or $73.7 per hour.

Sr Software Engineer, Embedded Machine Learning

Cariad, Inc.

Mountain View, CA

$146.30K - $191.70K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 2 days ago


Job description

We are CARIAD, an automotive software development team with the Volkswagen Group. Our mission is to make the automotive experience safer, more sustainable, more comfortable, more digital, and more fun. To achieve that we are building the leading tech stack for the automotive industry and creating a unified software platform for over 10 million new vehicles per year. We're looking for talented, digital minds like you to help us create code that moves the world. Together with you, we'll build outstanding digital experiences and products for all Volkswagen Group brands that will transform mobility. Join us as we shape the future of the car and everyone around it.

Role Summary

The Sr Software Engineer, Embedded Machine Learning is responsible for designing, optimizing, and deploying machine learning models on high-performance embedded hardware platforms. This role focuses on translating machine learning models from training environments into production-ready implementations on embedded ML accelerators, including selection of efficient model architectures, quantization, runtime performance analysis, and functional validation.

The Sr Software Engineer, Embedded Machine Learning works independently on complex technical problems and collaborates closely with software, hardware, and systems teams to ensure reliable, real-time performance of machine learning workloads in production embedded systems.

Role Responsibilities

Embedded ML Development & Optimization

  • Design, train, and optimize machine learning models for execution on embedded ML accelerators
  • Quantize and convert machine learning models from training frameworks to embedded runtime environments
  • Analyze and optimize runtime performance to meet real-time and hardware constraints
  • Develop and maintain production-quality code and artifacts supporting machine learning deployment on embedded systems

Validation & Production Support

  • Verify functional correctness and performance of deployed models on target hardware
  • Debug and resolve performance and accuracy issues across the machine learning deployment pipeline
  • Collaborate with cross-functional teams to integrate machine learning models into embedded systems
  • Support deployed machine learning models in production, including performance monitoring, issue triage, and iterative improvement

Technical Collaboration & Continuous Improvement

  • Contribute to continuous improvement of machine learning workflows, tools, and best practices
  • Share technical knowledge and lessons learned with peers
  • Document model behavior, performance characteristics, and deployment considerations to support collaboration and long-term maintainability

Years of Experience

  • 6+ years of experience in machine learning, embedded systems, or performance-critical software development
  • Production experience deploying and optimizing ML models on embedded or constrained hardware platforms

Required Education

  • Bachelor's degree in Computer Science or Computer Engineering

Desired Education

  • Master's degree in Computer Science or Computer Engineering

Skills

  • Strong analytical and problem-solving skills applied to complex, real-time systems
  • Ability to work independently on complex technical problems with limited supervision
  • Clear written and verbal communication skills for collaborating with cross-functional partners
  • Strong attention to detail and commitment to production-quality outcomes
  • Demonstrated ability to learn new technologies and share knowledge with peers

Required Skills

  • Training modern machine learning networks, including transformer-based architectures, for high-performance embedded hardware accelerators
  • Quantization, deployment, and optimization of machine learning models for production embedded systems
  • Profiling, debugging, and optimizing runtime performance of machine learning workloads on embedded ML accelerators
  • Supporting machine learning models through deployment, validation, and iterative improvement on target hardware

Desired Skills

  • Experience with Qualcomm Hexagon NPUs
  • Experience working in ADAS or automotive embedded systems environments

Work Flexibility

  • Some on-site work with embedded hardware required, driving test car

Compensation

Salary range is dependent on factors such as geographical differentials, credentials or certifications, industry-based experience, qualification and training. In the city of Mountain View, CA, the salary range for this position is $181,414 - $249,046.

CARIAD, Inc. provides performance based merits and annual bonus along with a competitive benefits package. Benefits include medical, dental, vision, 401k with employer match and defined contribution plan, short and long term disability, basic life and AD&D insurance, employee assistance program, tuition reimbursement and student loan repayment plans, maternity and non-primary caregiver leave, adoption assistance, employee referral program and vacation and paid holidays. We also offer a unique vehicle lease program that covers registration and insurance fees.

CARIAD is an Equal Opportunity Employer.  We welcome and encourage applicants from all backgrounds, and do not discriminate based on race, sex, age, disability, sexual orientation, national origin, religion, color, gender identity/expression, marital status, veteran status, or any other characteristics protected by applicable laws.

Employment with Cariad Inc. is contingent upon the successful completion of this screening process. We emphasize the importance of compliance with export control and sanctions laws as a fundamental aspect of our operations. Our company is dedicated to adhering to these regulations to ensure the lawful and ethical conduct of our business activities. Employment with our company is contingent on either verifying U.S. citizenship or U.S. lawful permanent resident status or obtaining any necessary license or confirming the availability of an applicable exemption or license exception. You, the applicant, will be required to answer certain questions for export control purposes, and that information will be reviewed by compliance personnel to ensure compliance with federal law. Cariad Inc. may choose not to apply for a license or use an applicable license exception (if available) for such individuals whose access to export-controlled technology or software source code may require authorization and may decline to proceed with an applicant on that basis alone.

By submitting your application, you acknowledge and agree to participate in the export control and sanctions compliance screening process. Your cooperation in this matter is essential to our shared success and the integrity of our operations. Thank you for your understanding and commitment to upholding these important standards.