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Mythic

Pioneering Analog Compute for AI

Compiler Engineer – Machine Learning Compiler

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteSeniorTeam 11-50Since 2012H1B SponsorCompany SiteLinkedIn

Location

California

Posted

1 day ago

Salary

Not specified

Seniority

Senior

Bachelor Degree3 yrs expEnglishC++PythonPyTorch

Job Description

• Join us in building the next generation of AI compilers. You’ll play a key role in developing the compiler for our novel AI accelerator, working side-by-side with hardware engineers and ML researchers. • Your work will shape how deep learning workloads run on cutting-edge dataflow hardware—defining the instruction set, execution model, and developer experience. • The result: a compiler that delivers breakthrough performance while remaining seamless and intuitive for ML developers. • Contribute across the full compiler stack, including operator lowering, graph/IR transformations, optimization passes, and backend code generation • Optimize for dataflow architectures, developing pipelined schedules, memory orchestration, and resource-constrained execution strategies • Collaborate with hardware architects to influence architectural features, ensuring the compiler and hardware evolve together • Develop compilation strategies that unify our analog compute with digital subsystems • Build and maintain a compiler that produces high-performance binaries with strong debugging support, clear error messages, and predictable performance models

Job Requirements

  • 3+ years of experience building compilers or high-performance systems software, especially those involving complex resource management or optimization.
  • Expert in modern C++ (C++14/17/20) and strong Python.
  • Experience with compiler IRs (SSA-based or graph-based), transformations, and code generation
  • Exposure to specialized accelerators (GPU, NPU, FPGA, or custom ASIC) or parallel architectures
  • Experience with machine learning compiler stacks (e.g., ONNX, MLIR, TVM, XLA, IREE, PyTorch), with contributions to MLIR or LLVM projects a plus
  • Experience with optimization methods (LP/MIP, CP, SAT/SMT) using solvers like Gurobi or OR-Tools for scheduling and resource allocation
  • Experience compiling for specialized accelerators (GPU, NPU, FPGA, or custom ASIC) on DNN workloads; GPU/DSP experience is valuable if combined with compiler backend work beyond kernel tuning
  • Familiarity with heterogeneous compilation, especially mixing custom accelerators with CPUs/GPUs/NPUs, and exposure to analog or in-memory compute is a plus
  • Experience collaborating in compiler–hardware co-design (architecture + ISA) for better compiler usability and hardware efficiency

Benefits

  • Competitive compensation, equity, and benefits package
  • A collaborative, innovative team that values engineering rigor, continuous integration, and user-focused design. We foster an environment of shared learning and technical excellence
  • The opportunity to shape how deep learning and LLM workloads are compiled on novel hardware.
  • A role that spans software and hardware co-design, shaping both the compiler and the accelerator architecture

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