Location: Zürich, Switzerland

Seniority: 5+ years (systems, compilers, or embedded)

Role

Join the core software team that turns ComputeRAM® into something developers love to use. You will design and ship a software integration kit, build the software library components that sit between applications and hardware, develop compiler passes that lower NN/DSP ops to our primitives, and own a solid benchmarking pipeline. You will work closely with silicon and equally closely with users, moving from prototype to production with clean code, clear documentation, and measurable speed and energy gains.

What you’ll do

• Design and implement embedded software libraries and low-level runtime for ComputeRAM-enabled platforms.
• Develop and maintain the compiler path (MLIR/LLVM passes, code generation, kernels) that maps AI and DSP primitives and related operations to our hardware.
• Develop and refine a benchmarking and profiling framework that incorporates reproducible tests, dashboards, and regression gates.
• Strengthen build, test, and CI so releases are predictable and artifacts are easy to consume.
• Collaborate with hardware, architecture, and customer-facing teams; write precise specs and documentation; turn feedback into roadmap items.

Outcomes (first 18 months)

• A production-ready driver + runtime stack for at least one MCU target and one accelerator-class target.
• A working compiler path with visible wins in latency and energy on representative models, documented end-to-end.
• A stable benchmark suite with automated reports and performance guards integrated in CI.
• Developer-quality docs, examples, and reference projects that make first use smooth for partners.

Requirements

• 5+ years building low-level software or compilers; strong C++ and Python; you have shipped production code.
• Hands-on experience with embedded systems and compiler design
• Solid systems understanding. memory and concurrency fundamentals
• Comfortable reading hardware datasheets and working at the HW/SW boundary.
• Evidence of performance work (profiling, tracing, optimization) on embedded or accelerator targets.
• Clear writing, good documentation habits, and a collaborative approach.

Nice to have

• Experience deploying deep-learning workloads on edge devices; familiarity with TensorFlow Lite for Micro, TVM, or IREE.
• HPC exposure (DirectML, OpenCL, CUDA) or DSP algorithm implementations.
• CI/CD depth and packaging for developer kits, utilizing GitHub Actions or a similar tool.
• Quantization and fixed-point experience for edge inference.