Infrastructure Software Engineer

Design and deploy a fully automated, scalable hybrid HPC cluster, combining bare-metal servers and switches with cloud instances, provisioned through MaaS and orchestrated via SLURM and Kubernetes, optimized for mixed EDA workloads and parallel CI pipelines.

Develop a real-time observability system for ASIC toolchain jobs and distributed builds, integrating Prometheus, Grafana, and VictoriaMetrics with streaming telemetry, tracing, and alerting to detect performance regressions before they hit silicon.

Architect and implement a programmable infrastructure-as-code control plane, using Terraform, Ansible, and Puppet, to version, audit, and redeploy every layer of Etched's development stack with deterministic reproducibility.

Create a zero-downtime interactive development environment that provisions and connects Jupyter and VS Code sessions to GPUs and high-memory nodes via a secure zero-trust network, abstracting away cluster state and machine failures.

Prototype and evaluate dynamic workload migration strategies between on-premise and cloud environments to optimize for latency, reliability, and cost across simulation and synthesis pipelines.

Design a synthetic testing and fault injection framework to validate the behavior of infrastructure under high-load, degraded hardware, and intermittent network partitions - before they happen in production.

Are a systems-minded software engineer who loves building foundational platforms, working close to the metal and cloud, solving high-leverage problems at scale.

Are a deeply technical engineer who treats infrastructure as a software problem - prioritizing clean abstractions, version control,small change lists, easy roll backs, testing, and long-term maintainability over ad hoc configuration.

Have strong programming skills in languages such as Python, Go, Rust, and C++, and are comfortable building production-grade tooling.

Possess expert-level knowledge of Linux, virtualization, containerization, and CI/CD pipelines, with a deep understanding of how to debug, optimize, and scale complex systems.

Are familiar with Infrastructure as Code tools like OpenTofu, Ansible, or Puppet, and enjoy designing declarative, reproducible infrastructure systems.

Understand and use PromQL and other telemetry/query languages and have used LLM to extract insight from real-time metrics, and know how to architect and tune observability stacks.

Have a track record of debugging and resolving difficult hardware-software integration problems across bare-metal systems, networks, and distributed workloads.

Can lead and mentor technical teams, guiding design decisions and helping others develop sound engineering instincts.

Have 8+ years of experience in infrastructure engineering, systems programming, or backend software development - ideally in environments where performance, scale, or hardware interaction mattered.

Are driven by curiosity, take initiative, and have an innate sense of ownership — you thrive in uncharted territory, design for edge cases, and love making systems more powerful, reliable, and elegant.

Familiarity with Bazel build system

Deep understanding of ASIC development flows, especially those involving Synopsys, Cadence, and Verilator, including how EDA tools interact with infrastructure for simulation, synthesis, and verification.

Hands-on experience architecting systems with AWS, GCP, or Azure, including hybrid on-prem/cloud deployments, workload migration strategies, and cloud-native orchestration tooling.

Experience monitoring, provisioning, and debugging bare-metal servers, network hardware, and high-performance storage systems in rack-scale environments.

Comfortable in profiling and optimizing compute environments for single-threaded latency, memory-bound workloads, or I/O throughput, especially in the context of simulation or CI performance.

Proficiency building or operating telemetry systems at scale using Prometheus, Grafana, Loki, VictoriaMetrics, and tools for distributed tracing, log aggregation, and real-time alerting across heterogeneous mediums (SMS, email, push alerts, etc.)

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.