Inference

Genesis AI; Northern (KY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Genesis AI is seeking an experienced ML infrastructure engineer to build low-latency inference pipelines and optimize distributed systems across GPU clusters for real-time robotics applications.

You will implement CUDA, Triton, and custom kernels, and integrate them into high-level frameworks, while developing monitoring tools to ensure reliability, determinism, and rapid diagnosis of regressions across both stacks.

Qualifications

• 8+ years of experience in distributed systems or ML infrastructure.
• Production-grade Python programming and experience with systems languages (C++, Rust, Go).
• Low-level performance mastery: CUDA, Triton, kernel optimization and memory management.

Responsibilities

• Build low-latency inference pipelines for on-device deployment and real-time control loops in robotics.
• Design and optimize distributed inference systems on GPU clusters for high throughput and efficient resource utilization.
• Implement efficient low-level code (CUDA, Triton, custom kernels) and integrate into high-level frameworks.
• Optimize workloads for throughput and latency (batching, scheduling, quantization, caching, memory management).
• Develop monitoring and debugging tools to guarantee reliability, determinism, and rapid diagnosis of regressions across stacks.

Skills

Distributed systems

ML infrastructure

High-performance serving

Python

C++/Rust/Go

Tools

CUDA

Triton

Kernel optimization

Quantization

Memory scheduling

GPU clusters

Job description

What You’ll Do

• Build low-latency inference pipelines for on-device deployment, enabling real-time next-token and diffusion-based control loops in robotics

• Design and optimize distributed inference systems on GPU clusters, pushing throughput with large-batch serving and efficient resource utilization

• Implement efficient low-level code (CUDA, Triton, custom kernels) and integrate it seamlessly into high-level frameworks

• Optimize workloads for both throughput (batching, scheduling, quantization) and latency (caching, memory management, graph compilation)

• Develop monitoring and debugging tools to guarantee reliability, determinism, and rapid diagnosis of regressions across both stacks

What You’ll Bring

• Deep experience in distributed systems, ML infrastructure, or high-performance serving (8+ years)

• Production-grade expertise in Python, with strong background in systems languages (C++/Rust/Go)

• Low-level performance mastery: CUDA, Triton, kernel optimization, quantization, memory and compute scheduling

• Proven track record scaling inference workloads in both throughput-oriented cluster environments and latency-critical on-device deployments

• System-level mindset with a history of tuning hardware–software interactions for maximum efficiency, throughput, and responsiveness