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Neuromorphic Engineering Jobs in California (NOW HIRING)

ML Compiler Engineer

San Bruno, CA · On-site

$100 - $150/hr

Our technology takes inspiration from the principles of neuromorphic computing such as sparsity to empower intelligence in everyday devices. We pioneered a high-performance AI accelerator integrated ...

Showing results 21-32

Neuromorphic Engineering information

See California salary details

$32.1K

$62.2K

$94.2K

How much do neuromorphic engineering jobs pay per year?

As of Sep 3, 2026, the average yearly pay for neuromorphic engineering in California is $62,152.00, according to ZipRecruiter salary data. Most workers in this role earn between $46,400.00 and $71,100.00 per year, depending on experience, location, and employer.

What is neuromorphic engineering?

A Neuromorphic Engineering job involves designing hardware and algorithms that mimic the structure and function of the human brain. Engineers in this field develop neuromorphic chips, spiking neural networks, and energy-efficient computing systems for tasks like artificial intelligence, robotics, and edge computing. Their work bridges neuroscience, computer science, and electrical engineering to create adaptive, low-power solutions for complex computations.

What skills and qualifications are needed for neuromorphic engineering?

To thrive in Neuromorphic Engineering, you need a strong background in electrical engineering, neuroscience, computer science, or a related field along with experience in designing algorithms and hardware modeled after neural systems. Familiarity with tools such as CAD software for circuit design, simulation platforms like MATLAB or Python, and knowledge of neuromorphic chips and systems is often required. Strong problem-solving abilities, creativity, and effective teamwork skills are highly valued in this interdisciplinary field. These competencies are essential for innovating and collaborating on the development of next-generation computing systems that bridge neuroscience and engineering.

What are common challenges faced by professionals in neuromorphic engineering?

Neuromorphic engineering professionals often encounter the challenge of translating complex biological neural processes into practical and scalable hardware and algorithms. Working in this field typically involves troubleshooting new or experimental designs, which can be more unpredictable and iterative than traditional engineering roles. Effective collaboration with neuroscientists, computer scientists, and engineers is crucial, as projects are highly interdisciplinary. Staying updated on rapid technological advancements and emerging research is also a key part of the job, requiring ongoing learning and adaptability.

How to become a neuromorphic engineer?

To become a neuromorphic engineer, one typically needs a bachelor's degree in electrical engineering, computer science, neuroscience, or a related field, followed by advanced education such as a master's or Ph.D. in neuromorphic systems, neural engineering, or machine learning. Developing skills in hardware design, programming (e.g., Python, C++), and understanding neural models and architectures is essential. Gaining experience through research projects, internships, or specialized training in neuromorphic hardware platforms like SpiNNaker or TrueNorth is also beneficial.

Which companies are working on neuromorphic engineering?

Several companies and research institutions are actively working on neuromorphic engineering, including Intel, IBM, BrainChip, and Qualcomm. These organizations develop neuromorphic chips and systems that mimic neural processes, often integrating specialized hardware and software for AI and machine learning applications.

What are the most commonly searched types of Neuromorphic Engineering jobs in California?

The most popular types of Neuromorphic Engineering jobs in California are:

What are popular job titles related to Neuromorphic Engineering jobs in California?

For Neuromorphic Engineering jobs in California, the most frequently searched job titles are:

Infographic showing various Neuromorphic Engineering job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 84% In-person, and 16% Remote job distribution, with an average salary of $62,152 per year, or $29.9 per hour.

Embedded Audio DL Engineer - Low-Power AI DSP

Femtosense

San Bruno, CA • On-site

$120 - $150/hr

Other

Medical, Vision, Retirement

Posted 2 days ago

New


Job description

San Bruno, United States | Posted on 03/12/2026

Headquartered in Silicon Valley, femtoAI—formerly known as Femtosense—was founded in 2018 by researchers from the Brains in Silicon Lab at Stanford University. Our technology takes inspiration from the principles of neuromorphic computing such as sparsity to empower intelligence in everyday devices.

We pioneered a high-performance AI accelerator integrated with an end-to-end embedded AI platform, enabling low-latency operation with less energy at a fraction of the cost. From wearables and household appliances to robotics and autonomous vehicles, femtoAI brings the power of AI to everyday devices.

Job DescriptionAbout the Role

As a Deep Learning Engineer, you will:

Design, develop, and deploy deep-learning-based and classical DSP audio algorithms for our SPU platform.

Leverage innovative model compression techniques to optimize performance-per-joule on custom silicon.

Be a core contributor to our deep learning and DSP development platform, advancing novel algorithms for audio processing.

Collaborate directly with customers across diverse industries to deliver impactful solutions and exciting new features.

This is a unique opportunity to work on challenging problems at the intersection of deep learning, model optimization, and embedded systems, while driving real-world impact in industries ranging from consumer electronics to automotive and beyond.

What You’ll Do

Develop and optimize deep learning models for audio processing, including tasks like speech enhancement, beamforming, event detection, sound localization, voice identification, voice interfaces, noise reduction, echo cancellation, feedback cancellation, and more.

Drive innovation in model efficiency, compression, and deployment on embedded platforms.

Leverage multi-sensor data to improve algorithm performance in difficult environments.

Work closely with customers to understand their needs and tailor solutions to meet their goals.

Contribute to the end-to-end process of model development, from research and prototyping to deployment on hardware.

Requirements

3+ years of relevant experience in deep learning and/or DSP engineering.

Strong experience with Python and PyTorch (or other deep learning frameworks).

A background in Computer Science, Mathematics, Electrical Engineering or a related field (BS, MS, PhD, or equivalent work experience).

Experience in hybrid AI / DSP algorithm development is a plus but not required.

A passion for solving challenging problems and collaborating in a fast-paced, innovative environment.

Desired Skills and Experience

Deep learning, Machine learning, DSP, Python, PyTorch

  • 401(k)
  • Medical insurance
  • Vision insurance
  • Disability insurance
  • Paid maternity leave
  • Paid paternity leave
  • Child care support

femtoAI is an equal opportunity employer committed to a diverse workforce which strives to create an inclusive working environment empowering everyone to do their best work. We do not discriminate on the basis of race, ethnicity, religion, gender, gender identity, sexual orientation, age, marital status, veteran status, or disability status.

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Femtosense logo

About Femtosense

Sourced by ZipRecruiter

Industry

Semiconductor and electronic component manufacturing

Company size

1 - 10 Employees

Headquarters location

San Bruno, CA, US

Year founded

2018