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Compiler Engineer

Job in Zürich, 8058, Zurich, Kanton Zürich, Switzerland
Listing for: microTECH Global LTD
Full Time position
Listed on 2026-01-30
Job specializations:
  • Software Development
    AI Engineer, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 30000 - 80000 CHF Yearly CHF 30000.00 80000.00 YEAR
Job Description & How to Apply Below
Location: Zürich

  • Design and implement embedded software libraries and low-level runtime for 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 Tensor Flow Lite for Micro, TVM, or IREE.
  • HPC exposure (Direct

    ML, OpenCL, CUDA) or DSP algorithm implementations.
  • CI/CD depth and packaging for developer kits, utilizing Git Hub Actions or a similar tool.
  • Quantization and fixed-point experience for edge inference.
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