Machine Learning Engineer - Kernels
About Mindbeam
We are building the next-generation AI infrastructure for open source and enterprise. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level.
Mission
Push the boundaries of performance by developing custom kernels and low-level optimizations for next-generation AI workloads.
Role Expectations
• Design and implement custom GPU/accelerator kernels to maximize performance.
• Profile, benchmark, and optimize critical ML workloads.
• Collaborate with researchers to translate algorithmic advances into efficient, production-ready code.
• Stay current with hardware advancements (CUDA, ROCm, TPU) to inform kernel design.
• Document and share best practices for low-level optimization.
Background
• Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or related field—or equivalent experience.
• 2+ years of experience in GPU programming, parallel computing, or systems-level optimization.
• Strong coding skills in C++, CUDA, or similar languages.
• Familiarity with ML frameworks and their low-level backends.
• Experience optimizing workloads for distributed and heterogeneous compute environments.
• Comfort with profiling tools and performance diagnostics.
About You
You are detail-oriented, performance-obsessed, and excited by the challenge of squeezing out every ounce of compute efficiency. You enjoy working at the intersection of algorithms and hardware, and you thrive in a collaborative environment where bold ideas are encouraged.
Apply To This Job
We are building the next-generation AI infrastructure for open source and enterprise. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level.
Mission
Push the boundaries of performance by developing custom kernels and low-level optimizations for next-generation AI workloads.
Role Expectations
• Design and implement custom GPU/accelerator kernels to maximize performance.
• Profile, benchmark, and optimize critical ML workloads.
• Collaborate with researchers to translate algorithmic advances into efficient, production-ready code.
• Stay current with hardware advancements (CUDA, ROCm, TPU) to inform kernel design.
• Document and share best practices for low-level optimization.
Background
• Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or related field—or equivalent experience.
• 2+ years of experience in GPU programming, parallel computing, or systems-level optimization.
• Strong coding skills in C++, CUDA, or similar languages.
• Familiarity with ML frameworks and their low-level backends.
• Experience optimizing workloads for distributed and heterogeneous compute environments.
• Comfort with profiling tools and performance diagnostics.
About You
You are detail-oriented, performance-obsessed, and excited by the challenge of squeezing out every ounce of compute efficiency. You enjoy working at the intersection of algorithms and hardware, and you thrive in a collaborative environment where bold ideas are encouraged.
Apply To This Job