As a Software QA Lead, you will focus on testing software compilers for AI workloads, particularly using PyTorch. You will design and maintain test suites, develop automated testing solutions, and collaborate with the compiler development team to ensure product quality and resolve issues. You will also hire and mentor a QA team.

As a Staff Software Engineer at d-Matrix, you will implement a unified performance modeling strategy for their simulations team, develop performance simulators in C++ and Python, and debug issues while leading junior engineers. Your role also includes correlating simulators with hardware implementations.

As a Staff Unix/Linux Infrastructure Engineer, you will manage and optimize Unix/Linux systems, ensuring scalability and high availability. You will work with cross-functional teams, leveraging expertise in networking, security, automation, and cloud platforms to troubleshoot, maintain, and enhance infrastructure.

Responsible for the micro-architecture and design of high-speed IO interfaces and high-performance PCIe Gen5 modules. Involves RTL design, verification, and participation in silicon validation, ensuring compatibility with industry standards and efficiency.

The Senior Staff Software Engineer for Kernels will develop and optimize software kernels for AI hardware. Responsibilities include mapping algorithms to hardware architectures, collaborating with teams on compiler infrastructure, and enhancing the software toolchain for AI compute engines, while possessing strong background in computational graph mapping and software development.

The Senior Principal RTL Digital Design Engineer will lead the micro-architecture and design of high-speed IO interfaces, ensuring high performance and efficient RTL design. Responsibilities include executing design verification, logic synthesis, and participating in silicon bring-up and validation.

As a Principal Machine Learning Research Scientist, you will research and develop efficient deep learning algorithms for d-Matrix's AI compute engine, collaborate with internal and external teams, optimize customer workloads, and contribute to research publications.

You will be responsible for developing, enhancing, and maintaining the software stack for AI hardware, including building optimized implementations of large language models, developing ML Op kernels, and working with experts in compiler and hardware architecture.

As the MLIR Software Engineering Technical Lead, you'll design and implement a compiler framework for NLP models on multi-chiplet architectures. Responsibilities include overseeing compiler development, collaborating with multiple teams on project milestones, and ensuring efficient task scheduling and data movement.

The ML Compiler Software Engineering Technical Lead will design and implement an MLIR-based compiler framework tailored for large-scale NLP models. Responsibilities include overseeing compiler development, managing task scheduling on processors, collaborating with various teams, and ensuring the overall efficiency of AI solutions. Technical leadership and team management experience are also essential.

As a Senior Staff ML Compiler Engineer, you will develop the compiler backend focusing on resource assignment in spatial architecture. Collaborate with a team to solve complex algorithmic problems, working on model partitioning, memory management, and compiler optimization, while delivering quality production code in C++.

The ML Compiler Engineer will focus on developing the compiler backend, addressing algorithmic compiler challenges, and optimizing hardware resource mapping for low-level instructions as part of a team of experienced developers.

The ML Compiler Engineer will develop the compiler backend by tackling algorithmic compiler problems, including hardware resource assignment for low level instructions. Responsibilities include model partitioning, scheduling, memory management, and optimization. Candidates should have a strong background in compiler infrastructure and machine learning frameworks, and be comfortable working in a fast-paced, dynamic environment.

The ML Compiler Engineer - Backend will develop the compiler backend, focusing on hardware resource allocation for executing low-level instructions. Responsibilities include model partitioning, memory management, and optimization. The role requires strong algorithmic problem-solving skills, expertise in modern C++, compiler infrastructures, and machine learning frameworks.

The ML Compiler Engineer will design and optimize high-level representations in a machine learning compiler. Responsibilities include transformations, dialect design, performance optimization, and model partitioning techniques. The role involves collaboration with compiler engineers and framework developers to ensure efficient execution of ML models.

The FPGA Design Engineer will design and verify FPGA solutions, define microarchitecture specifications, develop boot architecture, and integrate RISC-V cores for d-Matrix AI accelerators. Responsibilities include creating secure boot solutions, collaborating for hardware-software integration, and authoring Python scripts for automation.

As a Machine Learning Computer Architect at d-Matrix, you'll analyze and enhance ML workloads, contribute to hardware/software features for AI inference accelerators, keep up with the latest research, and collaborate with diverse teams to propose performance models and new features.

The Senior Emulation Engineer will create an emulation environment using advanced techniques, bring up SoC on the emulation platform, and collaborate with both design verification and software teams, requiring expertise in System Verification and scripting.

The Design Verification Engineer will be responsible for the entire SoC verification cycle, ensuring design integrity from architecture to execution. The role requires deep knowledge of verification methodologies, especially UVM and SystemVerilog, with a focus on AI applications in a fast-paced startup culture.

As a Staff Software Engineer, you will lead the architecture and development of a high-performance AI inference runtime, integrating it with PyTorch and collaborating with cross-functional teams to optimize performance and ensure code quality.