Inference

Genesis; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Genesis is seeking a senior engineering expert to build low-latency inference pipelines for on-device deployment and real-time control loops in robotics. You will design and optimize distributed inference on GPU clusters, push throughput with large-batch serving, and implement efficient low-level code integrated into high-level frameworks.

The role emphasizes a system-level mindset, with deep experience in CUDA/Triton kernel optimization and a proven track record scaling inference workloads in

Qualifications

• 8+ years of experience in distributed systems, ML infrastructure, or high-performance serving.
• Production-grade Python with strong background in systems languages (C++/Rust/Go).
• Experience optimizing CUDA/Triton kernels and memory scheduling.

Responsibilities

• 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 for high-throughput serving.
• Implement efficient low-level code (CUDA, Triton, custom kernels) and integrate into high-level frameworks.
• Optimize workloads for throughput and latency including caching and memory management.
• Develop monitoring and debugging tools to ensure reliability, determinism, and rapid diagnosis of regressions.

Skills

Distributed systems

ML infrastructure

High-performance serving

Python programming

Systems languages (C++/Rust/Go)

Tools

CUDA

Triton

GPU kernel optimization

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