Neuromorphic/AI Research Scientist

Intel Corporation•Austin, TX

4d•$170,500 - $315,490•Hybrid

About The Position

Intel's Neuromorphic Computing Lab has spent nearly a decade advancing brain-inspired computing with a global ecosystem of more than 250 research groups. Its Loihi research chips have pioneered event-driven, sparse, massively parallel neuro-inspired processing and supported more than 100 peer-reviewed publications. As part of Intel's CTO Office, the lab is working to bring these research advances into products for physical AI systems. The Neuromorphic AI Research Scientist will advance neuromorphic processor technology toward commercial adoption by modeling, prototyping, and defining architectures and algorithms for real-world customer applications in edge computing, signal processing, and autonomous systems. The position includes developing and evaluating research software and prototypes, collaborating with hardware and software teams, and communicating findings to technical and broader audiences.

Requirements

PhD or equivalent research experience in Computational Neuroscience, Computer Science, Electrical Engineering, Applied Mathematics, Robotics, or a related field.
At least one project demonstrating the ability to turn research ideas into working prototypes and evaluate them rigorously using benchmarks, baselines, ablations, and quantitative reporting.
At least three publications in leading journals or conferences on AI/ML, edge computing, robotics, and/or control.
At least two years of experience with state-of-the-art AI models and optimization, and at least one of control/dynamical systems, signal processing, or probabilistic inference.
At least two years of scientific research programming experience, such as Python or C/C++, including software development best practices such as object-oriented design, testing, debugging, documentation, version control, and code reviews.
Innovative problem-solving skills and the ability to approach complex challenges creatively.
Strong interpersonal skills and the ability to work effectively across diverse, multidisciplinary teams.
Adaptability in the evolving landscape of AI technologies.
Ability to communicate complex technical concepts to technical and non-technical audiences.
A results-oriented approach to translating research innovations into practical, commercial solutions.
Intellectual curiosity and an interest in continuous learning and neuromorphic computing.

Nice To Haves

Track record of open-source releases or widely used internal libraries.
Ability to write high-impact technical narratives, such as position papers.
At least one year of experience with a strong mathematical background.
At least one year of experience with edge AI hardware accelerators or event-based sensors.
At least one year of experience with quantization and low-precision arithmetic.
At least one year of experience developing software for robotics or other real-time control systems.
At least one year of experience building performant components in a systems language such as C/C++ or assembler, or an equivalent performance-oriented environment.
At least one year of experience profiling and benchmarking algorithms and hardware architectures.
At least one year of hardware architecture and design experience.

Responsibilities

Design and analyze neuromorphic AI algorithms for robotics, signal processing, control, and learning on edge platforms.
Develop research-quality software artifacts, including APIs, kernels, and benchmarking tools, to enable collaborative innovation.
Prototype and evaluate approaches under realistic constraints, including quantization, sparsity, memory footprint, latency, and energy optimization.
Collaborate with hardware and software engineering teams to translate algorithmic requirements into hardware specifications.
Build experimental pipelines and benchmarks for target use cases, including comprehensive baseline and ablation studies.
Communicate results through technical documentation, customer prototypes, and publications in leading conferences and journals.
Contribute technical thought leadership in neuromorphic computing and present findings to internal and external stakeholders.