1

Embedded Machine Learning Jobs (NOW HIRING)

Familiarity with embedded machine learning, real-time systems, or deploying machine learning on ... edge devices. * Background in adaptive modulation, beamforming, or cognitive radio techniques.

Stay current with the latest Machine Learning research for wireless and embedded systems. * Perform ... other related duties of which the above are representative. REQUIRED QUALIFICATIONS * Bachelor of ...

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Work with print software and embedded teams to integrate validated models into production code ...

Machine Learning Engineer

Fremont, CA · On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Work with print software and embedded teams to integrate validated models into production code ...

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Work with print software and embedded teams to integrate validated models into production code ...

next page

Showing results 1-20

Embedded Machine Learning information

See salary details

$70K

$153.4K

$174K

How much do embedded machine learning jobs pay per year?

As of Jun 24, 2026, the average yearly pay for embedded machine learning 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.

Will AI replace embedded programmers?

Embedded machine learning involves developing algorithms for resource-constrained devices, and while AI tools can assist with coding and optimization, embedded programmers are essential for designing, implementing, and maintaining these systems. AI is more likely to augment their work rather than fully replace them, especially given the need for specialized knowledge of hardware and real-time constraints.

Is embedded AI a good career?

Embedded machine learning involves developing AI models for hardware with limited resources, such as IoT devices and embedded systems. It is a growing field with demand for skills in hardware programming, C/C++, and AI frameworks, offering opportunities in industries like automotive, healthcare, and consumer electronics.

Is embedded systems still a good career in 2026?

Embedded Machine Learning remains a strong career in 2026 as industries increasingly adopt AI-powered devices and IoT solutions. Professionals with skills in hardware programming, real-time systems, and machine learning frameworks like TensorFlow Lite are in demand for developing intelligent embedded applications. Continuous learning and familiarity with microcontrollers, sensors, and embedded software development are essential for long-term growth in this field.

What engineers make $500,000?

Senior engineers in specialized fields such as embedded machine learning, AI, or data science can reach salaries of $500,000 or more, especially with extensive experience, advanced skills in programming and hardware, and leadership roles. High compensation often involves working in high-demand industries, with additional bonuses or stock options contributing to total earnings.

What are some common challenges faced by professionals working in embedded machine learning roles?

Professionals in embedded machine learning roles often face the challenge of optimizing machine learning models to run efficiently on resource-constrained hardware, such as microcontrollers or edge devices with limited memory and processing power. Balancing model accuracy, inference speed, and energy consumption can require creative problem-solving and deep knowledge of both hardware and software. Additionally, collaboration with hardware engineers, data scientists, and software developers is key, as projects typically require cross-functional teamwork to meet performance and deployment goals. Staying current with rapidly evolving tools and best practices is also important in this dynamic field.

What is an Embedded Machine Learning job?

An Embedded Machine Learning job involves developing and optimizing machine learning models to run efficiently on resource-constrained devices like microcontrollers, edge devices, and IoT hardware. Professionals in this role work on model compression, low-power inference, and real-time processing, ensuring AI capabilities can function without relying on cloud computing. Responsibilities often include data preprocessing, feature extraction, model training, and deployment on embedded systems using frameworks like TensorFlow Lite or Edge Impulse.

What are the key skills and qualifications needed to thrive in the Embedded Machine Learning position, and why are they important?

To thrive in Embedded Machine Learning, you should have expertise in machine learning algorithms, embedded systems programming (e.g., C/C++, Python), and a solid understanding of hardware-software integration, typically backed by a degree in computer engineering, electrical engineering, or a related field. Familiarity with edge AI tools (such as TensorFlow Lite, ONNX, or Edge Impulse), microcontrollers, and real-time operating systems is highly valued, alongside relevant certifications such as Embedded Systems or AI certificates. Strong problem-solving skills, effective communication, and the ability to work cross-functionally are crucial soft skills in this field. These qualifications and qualities are vital for creating efficient, reliable AI solutions that operate seamlessly within resource-constrained environments and interdisciplinary project teams.

More about Embedded Machine Learning jobs
What cities are hiring for Embedded Machine Learning jobs? Cities with the most Embedded Machine Learning job openings:
What are the most commonly searched types of Embedded Machine Learning jobs? The most popular types of Embedded Machine Learning jobs are:
What states have the most Embedded Machine Learning jobs? States with the most job openings for Embedded Machine Learning jobs include:
Infographic showing various Embedded Machine Learning job openings in the United States as of June 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 87% Physical, 2% Hybrid, and 11% 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

$146K - $191K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 28 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.