Machine Learning Engineer - Kernels

Mindbeam; United States; On-site; USD 100,000 - 140,000; Full time

Job summary

Mindbeam is seeking a talented GPU Programmer to enhance AI infrastructure. In this role, you will design custom GPU/accelerator kernels, optimize ML workloads, and work with researchers on efficient code solutions.

The ideal candidate holds a degree in Computer Science or similar and possesses 2+ years in GPU programming with strong skills in C++ and CUDA. Join us to push the boundaries of AI performance!

Qualifications

• 2+ years of experience in GPU programming, parallel computing, or systems-level optimization.
• Strong coding skills in C++, CUDA, or similar languages.
• Experience optimizing workloads for distributed and heterogeneous compute environments.

Responsibilities

• 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.

Skills

GPU programming

Parallel computing

C++

CUDA

Education

Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or related field

Tools

Profiling tools

ML frameworks

Job description

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.