ML Systems Engineer

About the role

We are a technology lab building and operating purpose-built AI products for life sciences. We're looking for a strong systems engineer to own the distributed stack that keeps our training, inference, and real-time agent execution reliable under production load.

Responsibilities

• Design and scale high-throughput inference engines and distributed serving runtimes
• Optimize memory, GPU kernels, and inter-node communication for real hardware efficiency
• Build and harden secure code-execution sandboxes for agent workflows
• Work side-by-side with product engineers to keep frontier-model behavior stable in regulated environments

Requirements

• Systems programming background (C++, Rust, or Python) and performance engineering experience
• Hands-on work with CUDA, Triton, Megatron-LM, SGLang, vLLM, or Ray
• Track record of shipping or maintaining high-performance ML infrastructure
• High ownership, fast iteration, zero tolerance for fragile shortcuts

Pay

$14,000 – $16,000 / month

Schedule

Hybrid (Dallas, TX)

Benefits

• Direct access to NVIDIA hardware and software stack (DGX, CUDA, Triton, NCCL)
• NVIDIA / Lead Provider benefits — cloud credits, early hardware access
• Real ownership of production systems used by regulated life-sciences customers
• Small team, high signal