AI Software Development Engineer - Neuromorphic Computing - California, Santa Clara, United States

Originally posted 23 September 2026 by the employer.

Help define how developers program a new class of AI hardware using neuromorphic computing.

About the role

As an AI Software Engineer in Neuromorphic Computing, you will turn new neuromorphic hardware capabilities into working, high-performance AI software. Your work will directly influence both the hardware and how developers use it by creating kernels, programming abstractions, and performance models for a new hardware architecture built around sparse, event-driven, and massively parallel computation.

What you'll do

• Design and implement specialized AI kernels for neuromorphic hardware using custom DSLs and accelerator programming models such as CUDA and SYCL.
• Develop and optimize operations for convolutional networks, transformers, and generative AI workloads by applying techniques such as tiling, fusion, vectorization, parallelization, layout transformation, buffering, sparsity, and quantization.
• Develop performance methodologies and improve kernel behavior in areas such as compute utilization, latency, throughput, memory bandwidth, data movement, synchronization, and scaling.
• Build reference models, numerical validation tools, benchmarks, and analytical or simulation-based performance, power, and area models that guide hardware-software co-design.
• Collaborate with hardware, compiler, runtime, and application engineers to deliver maintainable code, tests, documentation, benchmarks, and performance-regression infrastructure.

What you'll need

• PhD with no prior professional experience, Master's degree with 2+ years, or Bachelor's degree with 4+ years of relevant experience in Computer Science, Electrical Engineering, Computer Engineering, Applied Mathematics, Physics, or a related technical field.
• 4+ years of experience developing, debugging, and delivering maintainable software in Python and either C or C++.
• 2+ years of experience implementing and optimizing numerical, machine-learning, or high-performance computing kernels using parallel programming and an accelerator programming model (e.g., CUDA, SYCL, OpenCL) or a domain-specific language (e.g., Triton).
• 2+ years of experience developing, training, or evaluating AI algorithms and machine-learning models using a framework such as PyTorch, JAX, or TensorFlow.
• 2+ years of experience establishing numerical correctness and measuring performance using reference implementations, automated testing, benchmarking, profiling, or performance-regression infrastructure.

Nice to have

• Experience developing or optimizing workloads based on convolutional networks, transformers, generative AI, or other models relevant to edge and physical AI systems.
• Experience with compiler technologies or domain-specific language development, such as MLIR, LLVM, TVM, or Triton.
• Experience with hardware performance modeling, architecture simulators, or hardware-software co-design.
• Advanced experience with optimization techniques including kernel fusion, tiling, vectorization, quantization, sparsity, layout optimization, or double buffering.
• Experience developing software for spatial, dataflow, neuromorphic, or other emerging AI accelerator architectures.
• Experience contributing to collaborative or open-source software projects using code review, continuous integration, documentation, and testing practices.

Skills: neuromorphic computing, AI kernels, CUDA, SYCL, PyTorch