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Neuromorphic Engineering Jobs (NOW HIRING)

Intel

Neuromorphic/AI Research Scientist

Austin, TX

Job Details: Intel's Neuromorphic Computing Lab has been at the forefront of brain-inspired ... Collaborate with hardware and software engineering teams to translate algorithmic requirements into ...

New

Boise State University

Post Doctoral Research Fellow

Boise, ID · On-site

$46.60K - $63.30K/yr

Publish high-impact research in top-tier neuromorphic engineering, circuit design, and/or computing journals and conferences. * Collaboration: Work closely with materials teams in AWESOME Subprojects ...

Intel

Neuromorphic/AI Research Scientist

Folsom, CA

Job Details: Intel's Neuromorphic Computing Lab has been at the forefront of brain-inspired ... Collaborate with hardware and software engineering teams to translate algorithmic requirements into ...

New

Intel

Neuromorphic/AI Research Scientist

Phoenix, AZ

Job Details: Intel's Neuromorphic Computing Lab has been at the forefront of brain-inspired ... Collaborate with hardware and software engineering teams to translate algorithmic requirements into ...

New

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How much do neuromorphic engineering jobs pay per year?

As of May 30, 2026, the average yearly pay for neuromorphic engineering in the United States is $62,977.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,000.00 and $72,000.00 per year, depending on experience, location, and employer.

What is a Neuromorphic Engineering job?

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 are the key skills and qualifications needed to thrive in the Neuromorphic Engineering position, and why are they important?

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 some 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.
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Neuromorphic Engineering jobs near you
Infographic showing various Neuromorphic Engineering job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $62,977 per year, or $30.3 per hour.

Staff ML Scientist - Neuromorphic Computing & Spiking Neural Networks

Triangle Workforce

Durham, NC • Remote

Full-time

Posted 17 days ago

Job description

We are hiring a Staff ML Scientist to lead research and development of spiking neural network (SNN) architectures for ultra-low-power, real-time edge inference. This rare role sits at the convergence of computational neuroscience and production machine learning, requiring expertise in neuromorphic hardware platforms (Intel Loihi 2, BrainChip Akida, SynSense), temporal coding schemes, and spike-timing-dependent plasticity (STDP) learning rules.


You will design brain-inspired ML models that achieve orders-of-magnitude improvements in energy efficiency over traditional deep learning for time-series, sensor fusion, and event-driven vision applications.


Candidates must have hands-on experience training and deploying SNNs, deep familiarity with frameworks like Norse, snnTorch, or Lava, and ideally published work in neuromorphic engineering or computational neuroscience. PhD required. Experience bridging the gap between neuromorphic research prototypes and production-ready inference systems is critical.


Key Skills: Spiking Neural Networks, Neuromorphic Computing, Intel Loihi, snnTorch, Norse, Lava, Edge ML, Sensor Fusion, PyTorch, STDP


This is a remote-first position based in North Carolina.