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Position Description

Machine Learning Engineer

Job Type: Contract
Location: Remote
Hours: 20 hours per week
Compensation: $80–$100/hour

About the Role

We are seeking a Machine Learning Engineer with strong experience in CUDA and GPU performance optimization to support an applied AI team developing research-grade reinforcement learning environments, tasks, and evaluation benchmarks.

This is a hands-on, systems-focused position spanning machine learning, reinforcement learning, CUDA, GPU computing, and high-performance inference. You’ll work closely with the hardware and systems layers to improve the speed, efficiency, and reliability of large-scale AI experimentation.

Key Responsibilities

• Build and enhance reinforcement learning environments, tasks, and benchmarks.
• Develop, optimize, and debug CUDA kernels and GPU workloads.
• Improve GPU performance, including throughput, latency, memory efficiency, and scalability.
• Profile ML and inference systems to identify and resolve performance bottlenecks.
• Optimize AI inference and training workloads.
• Translate research prototypes into reliable, production-ready systems.
• Implement performance improvements across CUDA, GPU, and systems layers.
• Collaborate with ML researchers and engineers to accelerate experimentation.
• Benchmark and evaluate system performance using quantitative metrics.
• Support infrastructure for large-scale ML and reinforcement learning workloads.

Required Qualifications

• Professional or research experience in machine learning, deep learning, or AI systems.
• Strong hands-on experience with CUDA and GPU programming.
• Experience developing, profiling, debugging, or optimizing CUDA kernels.
• Experience optimizing GPU-based ML or inference workloads.
• Strong understanding of GPU architecture, memory utilization, parallelism, and performance.
• Experience with AI inference infrastructure and optimization.
• Familiarity with reinforcement learning concepts and workflows.
• Strong Python and/or C programming skills.
• Ability to independently troubleshoot complex performance issues.
• Comfortable working in a fast-paced, research-driven environment.
• Availability for at least 20 hours per week.

Preferred Qualifications

• Experience with PyTorch, TensorFlow, JAX, or similar ML frameworks.
• Experience with NVIDIA GPUs, Nsight Systems, or Nsight Compute.
• CUDA C/C development experience.
• Experience optimizing deep learning training or inference pipelines.
• Familiarity with distributed or large-scale GPU computing.
• Experience developing RL environments or benchmarks.
• Experience converting research code into reliable, maintainable systems.
• Graduate-level research or equivalent hands-on experience in ML, computer science, or a related field.

Why Join?

This opportunity offers the chance to work at the intersection of AI research, reinforcement learning, and high-performance GPU systems.

You’ll directly contribute to the kernels, inference infrastructure, tooling, and experimentation systems that help AI researchers and engineers run faster and more demanding workloads.

If you enjoy GPU optimization, solving performance challenges, and turning research ideas into efficient, reliable systems, we’d like to hear from you.