Artificial Intelligence, Machine Learning, Deep Tech, Software

Machine Learning Systems Engineer

San Mateo, CAOnsiteFull TimePosted 1 month ago

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Required skills

Rust
CUDA
PyTorch
Linux Performance
Production Ops
GPU memory pools
mixed precision
quantization
low-latency serving
custom CUDA kernels

Nice to have

candle
Burn
Triton
CUTLASS
TorchInductor
TensorRT

Role summary

Archetype AI is seeking a Machine Learning Systems Engineer to own the serving path for their physical AI platform and multimodal models. This role involves building and optimizing Rust-native inference runtimes, custom GPU kernels, and serving infrastructure to ensure high GPU utilization and low-latency inference. The ideal candidate has 6+ years of software engineering experience, with significant focus on ML systems, inference, and high-performance GPU computing, expert-level PyTorch knowledge, and strong CUDA or equivalent GPU expertise. Experience with Rust or C++, Linux performance, and production operations is essential.

About Job

At Archetype AI, we’re building the world’s first physical AI platform to bring artificial intelligence into the real world. Our foundation model, Newton, understands the physical world through objective sensor data and generates real-time insights into complex physical behaviors, from industrial machinery and systems to wearable devices and smart environments.

Formed by a high-caliber team from Google and backed by one of Silicon Valley’s most renowned venture funds, Archetype AI is in a Series A phase and rapidly advancing its technology for the next big leap. This is a unique opportunity to join an exciting, fast-growing AI team based in the heart of Silicon Valley.

About the Role

You will own the serving path for Newton and related multimodal models. Much of our inference stack is Rust-native: model nodes in our agent runtime, built on Rust ML stacks (candle, Burn) with custom GPU kernels, plus the routing layer that streams real-time inference to GPU nodes. You will drive GPU utilization, numerical precision, and low-latency serving from the kernel up.

What You'll Own

Build and own model nodes in our Rust inference runtime: loading, warmup, batching, streaming, GPU memory pools.
Optimize kernels and the GPU path: custom CUDA kernels, mixed precision, quantization, parity against research.
Own inference routing and serving: streaming path API to GPU node, request batching, SLO-backed latency and cost.
Productionize research checkpoints: export, compilation, quantization, parity evals, and rollout.
Build the observability inference needs: latency histograms, GPU metrics, OOM signatures, replayable traces.

Key Qualifications

6+ years software engineering, several of them in ML systems, inference, or high-performance GPU computing.
Has owned a production serving path end to end, not only benchmarked models.
Expert in PyTorch with models shipped to production; knows what will be slow before the profiler runs.
Strong CUDA or equivalent GPU depth: memory hierarchy, occupancy, Nsight or equivalent profiling.
Rust or C++ alongside Python, Linux performance, production ops; ready to work in Rust daily.
Works with researchers: can translate an architecture change into serving work.

Nice to Have

Rust ML stacks: candle, Burn, or comparable GPU compute in Rust.
Custom kernels and compiler stacks: Triton, CUTLASS, TorchInductor, TensorRT.
Quantization and mixed precision in production with a numerical-correctness suite.
Multimodal, video, embedding or time-series serving, not only decoder-only chat LLMs.
High-performance serving stacks (vLLM, SGLang, TensorRT-LLM): continuous batching, paged KV cache.

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