Diffusion-based LLM startup making AI radically faster and cheaper.
Machine Learning Systems Engineer
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You'll spend your days squeezing inference latency out of diffusion models—rewriting kernels, profiling memory bandwidth, and deciding when a custom CUDA op beats PyTorch's defaults. One morning might mean benchmarking a new attention variant on H100s; the afternoon, debugging why a quantized checkpoint OOMs at batch size eight. This is work where shaving milliseconds translates directly to dollars saved for customers running million-token generations.
What they're looking for
- 2–5 years shipping ML infrastructure in production, with solid footing in Python and at least one systems language (C/C++, Rust, or Go)
- Hands-on experience optimizing deep learning training or inference—kernel fusion, custom operators, memory planning, or distributed strategies you've actually debugged
- Comfortable navigating cloud GPU stacks: you've set up clusters on AWS, GCP, or Azure, and have opinions on Kubernetes schedulers for ML workloads
- Track record with at least one major framework (PyTorch or TensorFlow) including lower-level APIs like torch.fx, torch.compile, or XLA
- Genuine curiosity about diffusion architectures—you've read the papers, maybe reimplemented a sampler, and get why consistency models matter for throughput