5 hours ago
Remote, United States +2 moreMid Level
Base Salary
$180k - $270k/yr
Responsibilities
- Implement new Mojo language features on the MLIR-based compiler architecture.
- Design optimization passes for CPUs, GPUs, and hardware accelerators.
- Shape Mojo’s syntax, performance semantics, and overall developer experience.
- Collaborate with kernel authors, hardware engineers, researchers, Modular core teams, and the open-source community.
- Contribute across the Mojo language, intermediate representation, code generation, SDK, and user experience.
Requirements
- At least 3 years of experience working on compilers, runtimes, or language implementations.
- Hands-on mastery of C++.
- Experience with MLIR and LLVM.
- Experience optimizing code for CPUs, GPUs, or AI accelerator hardware.
- Contributions to a real-world compiler project.
- Python fluency is a plus.
Benefits
- Benefits may include comprehensive healthcare coverage, retirement and savings programs, employee stock purchase opportunities, paid time off, wellbeing resources, family support programs, and learning and development opportunities.
- The role can be performed from an office in Los Altos, California or Edinburgh, or remotely from home for candidates in the US, Canada, and UK.
- New-hire onboarding is conducted in person at the appropriate office.
- Regular team onsites and local meetups are offered, with expected travel 2–4 times per year.
- The compensation package may include RSU grants, annual target bonus, equity, and location-dependent benefits.
About Modular
Modular builds an AI developer platform for training and especially inference/serving, centered on the MAX runtime and the Mojo programming language. Its tools accelerate and deploy models from frameworks like PyTorch and TensorFlow on CPUs and GPUs, for teams running on cloud or on‑prem infrastructure. Founded in 2022, the company operates remote‑first with an office in Los Altos, CA, and sells a commercial platform and enterprise support to organizations productionizing generative and classical ML.