Remote | CUDA Engineering Expert — $60–$100/hour

24-Mag Llc; Northern, New York (KY, NY); Hybrid; USD 83,000 - 138,000; Full time

Job summary

24-MAG LLC invites experienced CUDA engineers for a fully remote contracting engagement focused on CUDA, C++, GLSL, and WebGPU. You will analyse GPU kernels, profile workloads, and push performance across modern GPUs.

Ideal candidates have a proven track record in HPC, memory and compute analysis, and strong communication across remote teams; no AI-training experience required. Compensation is hourly, with rigorous benchmarking and documentation of measurable improvements for stakeholders across

Qualifications

• Demonstrated CUDA programming expertise.
• Strong GPU kernel performance optimization track record.
• Advanced C++ development experience.
• Experience in high-performance computing environments.
• Hands-on GLSL and WebGPU experience.
• Proficient with GPU profiling tools (Nsight, Visual Profiler).
• Ability to analyze memory behavior and hardware utilization.
• Excellent written and verbal technical communication.
• Comfortable collaborating in remote, cross-disciplinary teams.
• No prior AI-training experience required.

Responsibilities

• Analyse and optimise GPU kernels using CUDA.
• Identify performance bottlenecks affecting throughput.
• Improve kernel efficiency across modern GPU hardware.
• Apply profiling-based optimisation techniques.
• Validate performance gains with benchmarks.
• Profile workloads with Nsight or Visual Profiler.
• Diagnose memory, compute, occupancy, and bottlenecks.
• Evaluate performance across hardware generations.
• Refactor CUDA and C++ code for maintainability.
• Develop GLSL shader logic and WebGPU workflows.
• Document optimisation strategies and results.
• Communicate GPU behavior to stakeholders.

Skills

CUDA programming

GPU kernel optimization

Advanced C++

HPC environments

GLSL

WebGPU

GPU profiling

Memory analysis

Tools

Nsight

Visual Profiler

Job description

We are sharing a specialised consulting opportunity for experienced CUDA Engineering Experts with strong expertise in CUDA, C++, GPU kernel optimisation, GLSL, WebGPU, profiling, and high-performance computing to contribute to an advanced GPU-engineering and AI training project.

Selected professionals will analyse and optimise GPU kernels, improve CUDA and C++ codebases, work with shader and WebGPU workflows, and document performance improvements across modern GPU architectures. The work requires strong low-level performance engineering, rigorous profiling, and the ability to translate technical findings into clear, actionable recommendations. No prior experience in AI is required.

Key Responsibilities

CUDA Kernel Analysis & Optimisation

• Analyse and optimise GPU kernels using CUDA
• Identify performance bottlenecks affecting computational throughput
• Improve kernel efficiency across modern GPU hardware
• Apply targeted optimisation techniques based on profiling results
• Validate performance gains using quantitative benchmarks

GPU Profiling & Performance Engineering

• Profile GPU workloads using tools such as Nsight, Visual Profiler, or comparable platforms
• Diagnose memory, compute, occupancy, and execution bottlenecks
• Evaluate kernel performance across different hardware generations
• Develop data-driven optimisation strategies
• Document measurable changes in latency, throughput, and resource utilisation

C++ & CUDA Codebase Refactoring

• Refactor CUDA and C++ code for improved maintainability and efficiency
• Improve architecture and organisation within performance-critical codebases
• Reduce unnecessary complexity while preserving functionality
• Adapt implementations for portability across GPU architectures
• Apply high-performance computing best practices throughout development

GLSL & WebGPU Development

• Implement shader logic using GLSL
• Develop graphics and compute workflows using WebGPU
• Integrate shader-based processing into existing systems
• Evaluate performance trade-offs across GPU execution environments
• Maintain compatibility and consistency across pipeline components

Technical Evaluation & Design

• Contribute expertise to GPU architecture and performance-design discussions
• Evaluate new GPU-based implementation approaches
• Assess technical trade-offs across performance, maintainability, and scalability
• Support definition of meaningful performance metrics
• Recommend practical approaches based on profiling and benchmarking evidence

Technical Documentation & Collaboration

• Document optimisation strategies, benchmark results, and technical findings
• Produce clear reports describing performance improvements
• Communicate complex GPU behaviour to technical stakeholders
• Collaborate with remote and cross-disciplinary project teams
• Share relevant developments in GPU programming and performance engineering

Ideal Profile

• Demonstrated expertise in CUDA programming
• Strong track record of GPU kernel performance optimisation
• Advanced C++ development experience
• Experience working in high-performance computing environments
• Hands‑on experience with GLSL and WebGPU
• Strong understanding of graphics or compute shader development
• Proficiency with GPU profiling tools such as Nsight, Visual Profiler, or equivalent
• Ability to reason about memory behaviour, kernel execution, and hardware utilisation
• Strong analytical skills for evaluating performance across GPU architectures
• Experience refactoring performance-critical codebases
• Excellent written and verbal technical communication skills
• Comfortable collaborating in remote, cross-disciplinary environments
• No prior AI‑training experience is required

Engagement Details

• Independent contractor engagement
• Fully remote
• Compensation: $60–$100/hour
• Work will involve CUDA kernel optimisation, GPU profiling, C++ development, GLSL, WebGPU, and performance analysis
• Strong low-level GPU performance expertise is central to this engagement
• Assignments may involve kernel benchmarking, bottleneck analysis, codebase refactoring, shader development, and architectural evaluation
• Project scope, workload, hardware targets, and performance requirements may evolve depending on project needs
• Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

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