Rust-powered ML Systems Engineer - GPU Inference

Pivots Hiring; San Mateo (CA); On-site; USD 190,000 - 260,000; Full time

Job summary

Archetype AI, a Silicon Valley AI startup, seeks a senior software engineer to own the serving path for Newton and related multimodal models. The role centers on a Rust‑based inference stack, optimizing GPU utilization, and delivering low‑latency serving from kernel to GPU node.

You will collaborate with researchers to translate architectural changes into production workloads, implement profiling and observability, and advance high‑performance serving stacks with modern CUDA and GPU techniques.

Qualifications

• 6+ years in software engineering with ML systems or high‑performance GPU computing.
• Owns production serving path end to end, not just benchmarks.
• Expert in PyTorch with production deployments; can anticipate slowdowns.
• Strong CUDA or equivalent GPU depth and profiling experience.
• Rust or C++ alongside Python; comfortable in Linux production ops.
• Collaborates with researchers to translate architecture changes into serving tasks.

Responsibilities

• Build and own model nodes in the Rust inference runtime for loading, warmup, batching, streaming.
• Optimize GPU kernels, memory usage, mixed precision, and quantization.
• Design and maintain inference routing and streaming path to GPU nodes with latency/SLO considerations.
• Productionize checkpoints: export, compilation, parity evals, and rollout.
• Develop observability: latency histograms, GPU metrics, OOM traces.

Skills

ML systems

GPU computing

PyTorch

CUDA

Rust

C++

Python

Linux

Production ops

Profiling

Tools

Nsight

Triton

CUTLASS

TensorRT

Job description

Archetype AI, a Silicon Valley AI startup, seeks a senior software engineer to own the serving path for Newton and related multimodal models. The role centers on a Rust‑based inference stack, optimizing GPU utilization, and delivering low‑latency serving from kernel to GPU node.

You will collaborate with researchers to translate architectural changes into production workloads, implement profiling and observability, and advance high‑performance serving stacks with modern CUDA and GPU techniques.