MS or PhD in Computer Engineering, Computer Science, or Electrical Engineering
5+ years of industry experience as a compiler engineer or developer
Experience developing compilers for GPU, dataflow, or machine learning applications
Startup mindset or experience is preferred
Strong understanding of compiler optimization techniques
Responsibilities
Design and maintain compiler toolchains for translating machine learning models into optimized workloads
Develop runtime systems and software libraries for efficient AI application deployment
Implement compiler optimizations to enhance performance and energy efficiency
Research innovative techniques to improve machine learning inference metrics
Collaborate with machine learning engineers for model conversion and optimization
Partner with hardware architects to co-design software and hardware features
Develop tools for performance analysis and benchmarking of AI workloads
Integrate industry-standard machine learning frameworks and formats
Lead technical design reviews and establish best practices for software development
Mentor junior engineers and contribute to the long-term roadmap for compiler technologies
Benefits
Opportunity to work on cutting-edge analog in-memory computing technology
Collaborative environment with cross-functional teams
Potential for significant impact in a startup setting
Mentorship opportunities for career growth
Access to industry-standard tools and frameworks
Full Job Description
Responsibilities:
Design, develop, and maintain compiler toolchains that translate machine learning models from industry-standard frameworks into optimized workloads for TetraMem's analog in-memory computing hardware.
Develop runtime systems, software libraries, and SDK components that enable efficient deployment, execution, and management of AI applications on TetraMem accelerators.
Implement compiler optimizations, including graph transformations, operator fusion, memory optimization, scheduling, and code generation to maximize performance and energy efficiency.
Research and develop innovative techniques to improve machine learning inference speed, latency, throughput, and power consumption across a wide range of AI workloads.
Collaborate closely with machine learning engineers to support model conversion, validation, optimization, benchmarking, and deployment.
Partner with hardware architects and silicon engineering teams to co-design software and hardware features that improve system performance, programmability, and usability.
Develop performance analysis, profiling, debugging, and benchmarking tools to evaluate and optimize AI workloads on current and future TetraMem platforms.
Integrate and support industry-standard machine learning frameworks and model formats, including PyTorch, TensorFlow, ONNX, and other emerging AI ecosystems.
Lead technical design reviews, contribute to software architecture decisions, and establish best practices for scalable, maintainable, and high-quality software development.
Mentor junior engineers, contribute to technical documentation, and help define the long-term roadmap for TetraMem's compiler, runtime, and SDK technologies.
Requirements:
MS or PhD in Computer Engineering/CS/EE
5+ years industry experience as a compiler engineer or developer
Experience developing compilers for GPU, dataflow compilers, or ML compilers
Startup mindset/experience
Experience in one or more of the following areas considered a strong plus:
Experience in RISC-V CPU/VPU kernel development and optimization
Experience providing technical leadership and/or guidance to other engineers
Knowledge of popular CPU/GPU compilers such as GCC, Clang
Knowledge of ML compilers such as MLIR
Experience with LLVM and other open-source compiler libraries and tools
Publications on compilation of ML or dataflow programs for HW acceleration
