Product Engineer

About Etched

Etched is building the world’s first AI inference system purpose-built for transformers - delivering over 10x higher performance and dramatically lower cost and latency than a B200. With Etched ASICs, you can build products that would be impossible with GPUs, like real-time video generation models and extremely deep & parallel chain-of-thought reasoning agents. Backed by hundreds of millions from top-tier investors and staffed by leading engineers, Etched is redefining the infrastructure layer for the fastest growing industry in history.

Job Summary

As Etched prepares to scale up customer deployments, we are seeking technical, product-focused members of the team who will build the golden path from installation to first token generated. You’ll build quick-start guides, Sohu SDK flows, reproducible performance harnesses for models, and much more, working across the software, architecture, and platform teams. This is a hands-on technical role that requires expertise in inference systems, performance, and datacenter infrastructure.

Key responsibilities

• Work with the Etched software team to define and document each layer of the Sohu software stack, from on-chip transaction execution to multi-rack workload sharding and management

• Define and build quick-start guides; establish the customer experience from installation to first inference request

• Develop performance harnesses for reference models and publish reproducible metrics

• Contribute to GA acceptance suite used in-house and at customer sites

• Curate and present demos of the emulation platform to customers; interface with customers to make and recommend use cases to engineering teams

• Collaborate with Tech Writers and engineering leads to maintain a versioned source of truth for documentation

• Drive design partner success: work alongside Customer Engineering team to triage field issues and unblock customer site installs and integration

You may be a good fit if you have

• Bachelor's degree or equivalent practical experience

• 5+ years of experience in software engineering, with a strong emphasis on ML inference infrastructure and systems

• Proficiency with modern web frameworks (e.g., React, Next.js, or similar) and backend infrastructure (e.g., Python, Node.js)

• Familiarity with inference serving stacks (vLLM, SGLang), ML frameworks (e.g., PyTorch, TensorFlow), and AI hardware acceleration (e.g., CUDA, ROCm, other GPGPU paradigms)

• Technical depth: Deeply understands (or can quickly learn) how AI computing infrastructure works from an application layer, software stack, ML research, and data center perspective

• Proactive self-starter: Can work across teams to assemble materials, gather data, run meetings, and more with minimal assistance

• Opinionated: Spots problems, speaks up when disagreeing, takes ownership, and can handle making important decisions

Strong candidates may also have experience with

• Deploying AI applications and infrastructure in the cloud or with on-prem compute clusters

• Experience with inference runtime behavior (KV caching, batching, tensor/pipeline parallel)

• Working with APIs, containerization, CI/CD pipelines, and cloud infrastructure (e.g., Docker, Kubernetes, AWS/GCP)

• Infrastructure for distributed computing or datacenter-scale systems

Benefits

• Full medical, dental, and vision packages, with generous premium coverage

• Housing subsidy of $2,000/month for those living within walking distance of the office

• Daily lunch and dinner in our office

• Relocation support for those moving to San Jose (Santana Row)

Compensation

• $175,000 - $225,000

How we’re different

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 San Jose (Santana Row), 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.