New Grads Computer/Electrical Engineer — Physical AI Compute

What You’ll Do

Build architectural models, simulators, and performance-analysis tools for the X²PU architecture.
Contribute to compiler, runtime, SDK, and developer-tooling infrastructure for programming and optimizing X²PU workloads.
Help map Physical AI workloads onto a new computer architecture, including signal processing, AI/ML, linear algebra, optimization, and control workloads.
Develop and optimize software kernels, libraries, APIs, examples, and application demos.
Work on software tools that help evaluate latency, throughput, memory movement, utilization, power, and performance tradeoffs.
Collaborate with architects, chip designers, firmware engineers, and application developers to connect hardware capabilities with software usability.
Prototype Physical AI applications in areas such as sensing, perception, autonomy, robotics, wireless, or real-time edge AI.
Integrate with or build around modern AI and compiler ecosystems where appropriate.
Write clean, well-tested, well-documented code that can grow into production-quality software infrastructure.
Participate in technical design reviews and help shape how developers program a new X²PUplatform.

What We’re Looking For
B.S. or M.S. in Computer Engineering, Electrical Engineering, preferred otherwise a related field, or equivalent hands-on project/internship experience.
Strong programming skills in C/C++ and Python.
Strong fundamentals in data structures, algorithms, systems programming, debugging, and software engineering.
Familiarity with computer architecture concepts such as memory hierarchy, parallelism, instruction execution, vector/SIMD processing, accelerators, GPUs, DSPs, or embedded systems.
Familiarity with compilers, runtimes, SDKs, AI frameworks, or performance modeling.
Comfort working in Linux development environments using tools such as Git, build systems, debuggers, profilers, and test frameworks.
Strong analytical ability and interest in performance, efficiency, latency, and real-time system behavior.
Clear written and verbal communication skills, including the ability to explain technical tradeoffs.
High ownership, intellectual curiosity, and a desire to learn from experienced engineers.
RTL, System Verilog, or hardware design experience is not required for this role. Curiosity about hardware/software co-design and computer architecture is important.

Preferred Qualifications

Experience with compiler infrastructure such as LLVM, MLIR, TVM, IREE, Halide, XLA, or similar systems.
Experience with AI frameworks or model formats such as PyTorch, TensorFlow, JAX, ONNX, or related deployment tools.
Experience with performance modeling, architecture simulation, workload analysis, or benchmarking.
Experience optimizing software for GPUs, DSPs, CPUs, NPUs, FPGAs, or other accelerators.
Familiarity with CUDA, OpenCL, SYCL, SIMD/vector programming, embedded software, or low-level performance optimization.
Interest in real-time systems, robotics, autonomy, edge AI, signal processing, radar/RF sensing, control systems, or physical-world computing.
Experience with numerical computing, linear algebra, FFTs, filters, optimization algorithms, or ML inference.
Research, internship, open-source, compiler, systems, AI, robotics, or architecture project experience.