Inference Software Engineer

Proficiency in Rust and/or C++

Good familiarity with PyTorch and/or JAX.

Good familiarity with transformers architectures

Ported applications to non-standard or accelerator hardware platforms.

Solid systems knowledge, including Linux internals, accelerator architectures (e.g., GPUs, TPUs), and high-speed interconnects (e.g., NVLink, InfiniBand)

Developed low-latency, high-performance applications using both kernel-level and user-space networking stacks.

Deep understanding of distributed systems concepts, algorithms, and challenges, including consensus protocols, consistency models, and communication patterns.

Solid grasp of large language model architectures, particularly Mixture-of-Experts (MoE).

Experience analyzing performance traces and logs from distributed systems and ML workloads.

Built applications with extensive SIMD (Single Instruction, Multiple Data) optimizations for performance-critical paths.

Familiar with cluster orchestration tools (e.g., Kubernetes, Slurm) and ML platforms (e.g., Ray, Kubeflow)

Experience designing and implementing CI/CD pipelines for MLOps workflows.

Etched believes in the Bitter Lesson . We think most of the progress in the AI field has come from using more FLOPs to train and run models, and the best way to get more FLOPs is to build model-specific hardware. Larger and larger training runs encourage companies to consolidate around fewer model architectures, which creates a market for single-model ASICs.

We are a fully in-person team in West San Jose, and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both as needed.