Persimmons AI

Compiler Engineer (Mid and/or Backend)

San Jose, California, United States · Mid

Sponsorship not specifiedDetected 143 days ago

PythonC++ExpressAlgorithmsMachine LearningPyTorchLLMsResearchCollaboration

About the role

Why join us: We're growing fast and looking for bold thinkers, builders, and curious problem-solvers who want to push the limits of AI hardware and software.
If you're ready to join a world-class team and play a critical role in making a global impact - we want to talk to you.
Summary of Role: This role focuses on transforming higher-level MLIR-based large language models by applying sophisticated mid- and backend compiler techniques to target Persimmons.ai's custom accelerator hardware.

Develop and enhance MLIR-based compiler pipelines targeting Persimmons' custom spatial accelerator hardware.
Design and optimize the Persimmons Compiler mid- and backend techniques for efficient lowering, graph-to-resources mapping, and code generation.
Implement transformations to convert Python, PyTorch, and similar kernel representations to LLVM IR and runtime-ready libraries.
Architect and implement efficient support for SPMD-based, distributed collective operations and lower them through specialized MLIR compiler dialects (e.g., MESH, SHARDY).
Drive advanced loop optimizations leveraging polyhedral analysis: loop tiling, fusion, interchange, skewing, and related techniques.
Apply and optimize techniques such as bufferization, padding, inlining, and integration of custom operations and kernels within the compilation workflow.
Collaborate across teams to deliver performant compilation flows from high-level ML representations to low-level executable artifacts.
You will help design and optimize the Persimmons Compiler mid- and backend, integrate it with custom operations and kernels, as well as implement compiler passes that convert higher-level intermediate representations into runtime-oriented code and libraries.

strong candidates will demonstrate expertise in several key areas.
Solid understanding and experience with underlying principles and methods of the MLIR framework (SSA representation, interfaces, rewriting, dialect hierarchy, etc.).
Hands-on experience with developing MLIR-based compiler infrastructure, algorithms, and techniques for non-GPU/custom spatial hardware architectures.
Working experience with lowering SIMD operations from PyTorch, Triton, xDSL, pyDSL, or similar Python-based frontends toward LLVM IR and, further, to SIMD kernel library.
Experience and understanding of SPMD-based, distributed collective operations, specialized MLIR compiler dialects (e.g., MESH, SHARDY), and collective operation lowering in compilers for spatial hardware.
Experience with techniques such as padding, bufferization, inlining, and other lowering techniques.
Knowledge of register allocation and instruction scheduling in spatial architectures.
Experience in lowering and integration of custom operations and kernels at the compiler mid- and backend.
Familiarity with graph and tensor partitioning and mapping optimization algorithms and their integration in the compiler workflow.
High level of understanding and 5+ years of experience with C++ and appreciation for writing clean and maintainable code.

Persimmons is building the infrastructure that will power the next decade of AI.
Founded in 2023 by veteran technologists from the worlds of semiconductors, AI systems, and software innovation, We're on a mission to enable smarter devices, more sustainable data centers, and entirely new applications the world hasn't imagined yet.
This position offers the opportunity to directly shape Persimmons.ai's innovative AI hardware and software stack through close collaboration with teams across hardware, systems, and software.
Work on register allocation and instruction scheduling for Persimmons' spatial hardware, ensuring high resource utilization, throughput, and low latency.
Contribute to graph and tensor partitioning logic for optimal hardware-targeted execution.
What You Bring To The Table: We do not expect candidates to meet all of the requirements listed below; strong candidates will demonstrate expertise in several key areas.
Extensive experience and understanding of loop optimization based on polyhedral principles.
Demonstrated fluency with modern AI tools and workflows (e.g., leveraging AI assistants for research, analysis, or productivity).
Competitive salary and benefits package.

This listing is sourced directly from Persimmons AI's careers page and normalized into a canonical job model.