Senior ML Systems Engineer — C++/Rust

A high-performance computing startup is building cutting-edge machine learning infrastructure that reimagines how models are composed, optimized, and deployed across diverse hardware targets. The team is developing a powerful intermediate representation and infrastructure stack — an “LLVM for Neural Networks” — designed to enable deep optimization, modularity, and cross-framework portability for modern ML workloads.

We’re looking for ML systems engineers who thrive at the intersection of hardware-aware compilation and low-level systems engineering. This role is ideal for someone with a strong background in infrastructure internals, C++ or Rust development, and performance optimization. You’ll work across the infrastructure stack — from intermediate representation design to backend code generation — to unlock efficiency on modern accelerators including GPUs, TPUs, and NPUs.

📍Ankara / On-site

Responsibilities

  • Design and optimize backend code generation paths targeting CPUs, GPUs, TPUs, and custom accelerators

  • Extend the intermediate representation to support hardware-specific features

  • Contribute to a high-performance ML infrastructure stack using C++ and/or Rust

  • Implement optimization passes for operator fusion, memory layout planning, and static/dynamic scheduling

  • Integrate with low-level runtime and execution engines (e.g., CUDA, ROCm, XLA)

  • Collaborate with upstream open-source communities (e.g., MLIR, TVM, Halide)

 Requirements

  • 3-5+ years of experience in systems programming, infrastructure development, or ML infrastructure

  • Strong proficiency in C++ and/or Rust for performance-critical development

  • In-depth knowledge of infrastructure architecture, IR transformations, and code generation

  • Familiarity with ML compilers and IR frameworks (e.g., LLVM, MLIR, TVM)

  • Solid understanding of hardware architecture and parallel computing models

  • Experience with numerical computation or symbolic graph-based systems

Nice to Have

  • Experience optimizing for GPUs or ML accelerators (e.g., Tensor Cores, NPUs)

  • Contributions to open-source ML infrastructure or compiler stacks

  • Background in custom runtime scheduling or memory management systems

  • Exposure to embedded or edge ML environments (e.g., low-power inference, mobile devices)

APPLICATION FORM