Location: US (Hybrid)About the RoleBe part of the team creating the software foundation for next-generation AI compute platforms. In this role, you'll work on compiler technologies, AI runtimes, graph optimization, and hardware-aware execution in close collaboration with inference engineers, ML scientists, and hardware specialists. You'll leverage modern AI technologies and methodologies to accelerate software development, improve software-hardware co-design, and optimize the deployment and execution of AI workloads on next-generation compute platforms.
This position offers the opportunity to contribute to state-of-the-art AI infrastructure, optimize software for emerging AI hardware, and help define how modern machine learning workloads are represented, compiled, and executed at scale.
We are particularly interested in engineers who have applied AI technologies to solve complex systems, compiler, runtime, or hardware challenges, rather than solely using AI as a software productivity tool.
How You'll Contribute- Build and optimize compiler and runtime infrastructure for modern AI workloads
- Enable efficient execution of machine learning models across GPUs, NPUs, TPUs, and custom AI accelerators
- Apply modern AI technologies and methodologies to improve software development, system optimization, and software-hardware co-design processes
- Collaborate with inference, systems, and hardware teams to improve software-hardware co-design
- Investigate and resolve performance bottlenecks through profiling, benchmarking, and system-level analysis
Required Skills- BSc, MSc, or PhD in Computer Science, Engineering, Mathematics, or a related discipline
- Strong programming skills in C/C++ and/or Python in Linux environments using common development tools
- Solid understanding of machine learning fundamentals and modern AI workloads
- Experience applying AI technologies to software engineering, performance optimization, and hardware-aware design challenges
Examples of Relevant Backgrounds Include- AI compiler frameworks and infrastructure (e.g., Mojo/Max, MLIR, LLVM, XLA, OpenXLA, Triton, Gluon)
- Compiler optimizations such as operator fusion, graph transformations, scheduling, code generation, and lowering pipelines
- AI runtimes and execution frameworks (e.g., ONNX Runtime, TensorRT, TVM Runtime, IREE Runtime, XLA)
- Deep understanding of AI systems, with experience applying AI technologies to solve engineering, performance, systems, or hardware-software optimization challenges
- Performance optimization of machine learning workloads on GPUs, TPUs, NPUs, DSPs, or custom accelerators
- Hardware-aware software development and AI accelerator enablement
- Development of high-performance kernels and operators (e.g., GEMMs, convolutions, attention, normalization, quantization)
- Distributed AI training or inference systems
- Model execution frameworks such as Max, PyTorch, TensorFlow, JAX, or ONNX
Nice to Have- Experience with Modular (Mojo/Max), OpenXLA, StableHLO, Torch-MLIR, Triton, TVM, or IREE
- Experience developing software for AI accelerators or machine learning hardware platforms
- Contributions to open-source projects such as LLVM, MLIR, PyTorch, OpenXLA, Triton, Gluon, or xDSL
- Experience building software for AI accelerators or AI compute platforms
Location & Travel- Hybrid role based in US
- Ability to travel periodically for collaboration with global teams and stakeholders
- International travel may be required
Compensation & Benefits Our benefits package for this position includes medical, dental and vision coverage; 401(k) with Company match; unlimited vacation, paid holidays; paid leave for personal events; parental benefits and employee assistance program.
All compensation and benefits are subject to the terms of the applicable plan documents and Company policies, which may be amended or discontinued at the Company's discretion. Eligibility for and the value of any bonus or equity award is not guaranteed.
