Neuromorphic/AI Research Scientist
Intel
- Location
- US California Santa Clara
- Work model
- On-Site
- Level
- Mid
- H-1B history
- 1,112 approvals (FY2023)
- Posted
- Sep 17, 2026
Skills
About this role
Job Details
Job Description: Intel's Neuromorphic Computing Lab has been at the forefront of brain-inspired computing for nearly a decade, working alongside a global ecosystem of 250+ research groups. Our groundbreaking Loihi series research chips have pioneered event-driven, sparse, and massively parallel neuro-inspired processing, resulting in over 100 peer-reviewed publications. As part of Intel's CTO Office, we're now transforming these research breakthroughs into real-world products that will power the next generation of physical AI systems. We are seeking a Neuromorphic AI Research Scientist to advance state-of-the-art neuromorphic processor technology toward commercial adoption. This role focuses on modeling, prototyping, and defining architectures and algorithms that enable transformative gains for real-world customer applications in edge computing, signal processing, and autonomous systems.
Key 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 that 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 with comprehensive baseline and ablation studies Communicate results through technical documentation, customer prototypes, and publications in leading conferences and journals Contribute to technical thought leadership in neuromorphic computing and present findings to internal and external stakeholders As a successful candidate, you must possess Innovative Problem-Solving: Skills to approach complex challenges with creative, out-of-the-box thinking Collaborative Leadership: Strong interpersonal skills to work effectively across diverse, multidisciplinary teams Adaptability: Flexibility to navigate the dynamic landscape of emerging AI technologies Communication Excellence: Skills to articulate complex technical concepts to both technical and non-technical audiences Results-Oriented Mindset: Drive to translate research innovations into practical, commercial solutions Intellectual Curiosity: Passion for continuous learning and staying at the cutting edge of neuromorphic computing Join Intel's pioneering team and help define the next wave of AI technology that will transform industries and create new possibilities. Apply today to be part of this groundbreaking journey from research to real-world impact.
Qualifications
You must possess the below minimum qualifications to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates. Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and or schoolwork/classes/research. Minimum Qualifications: - PhD (or equivalent research experience) in Computational Neuroscience, Computer Science, Electrical Engineering, Applied Mathematics, Robotics, or a related field. - 1+ projects demonstrating the skills to turn research ideas into working prototypes and to evaluate them rigorously (benchmarks, baselines, ablations, quantitative reporting). - 3+ publications in leading journals or conferences on AI/ML, edge computing, robotics, and/or control. - 2+ years of experience with: State-of-the-art AI models, optimization, and at least one of: control/dynamical systems, signal processing, or probabilistic inference. Scientific programming for research (e.g., Python or C/C++) including experience with software development best practices (OO design, testing, debugging, documentation, version control, code