Supercomputing Engineer (Network)

About Etched

Etched is building hardware for frontier intelligence. We co-design chips, racks, software, and manufacturing to deliver best-in-class throughput and latency across both prefill and decode workloads. Our first products are heavily focused on inference. 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

We are seeking highly motivated and skilled Supercomputing Engineers (Network) to join our team. This team plays a critical role in developing, qualifying, and optimizing high-performance networking solutions for large-scale inference workloads. As a Pod Software Engineer, you will focus on developing and qualifying software that drives communication amongst our inference nodes in multi-rack inference clusters. You will collaborate closely with kernel, platform, and telemetry teams to push the boundaries of peer-to-peer RDMA efficiency.

Key Responsibilities

• Design, develop, and implement RDMA based networking peering, supporting high bandwidth, low latency communication across PCIe nodes within and across racks. Includes work across Operating System, kernel drivers, embedded software and system software.

• Develop tests that qualify host processors (x86),. NICs, TORs and device network interfaces for high performance.

• Furnish burn-in teams with tests that represent both real-world use cases and workloads for device to device networking, and extreme-load stress testing. 

• Define the key metrics that system software must collect to maintain high availability and performance under extreme communications workloads.

Representative Projects

• Analyze performance deviations, optimize network stack configurations, and propose kernel tuning parameters for low-latency, high-bandwidth inference workloads.

• Design and execute automated qualification tests for RDMA NICs and interconnects across various server configurations.

• Identify and root-cause firmware, driver, and hardware issues that impact RDMA performance and reliability.

• Collaborate with ODMs and silicon vendors to validate new RDMA features and enhancements.

• Implement and validate peer RDMA support for GPU-to-GPU and accelerator-to-accelerator communication.

• Modify kernel drivers and user-space libraries to optimize direct memory access between inference pods.

• Profile and benchmark inter-node RDMA latency and bandwidth to improve inference job scaling.

• Optimize NIC and switch configurations to balance throughput, congestion control, and reliability.

You may be a good fit if you have

• Proficiency in C/C++

• Proficiency in at least one scripting language (e.g., Python, Bash, Go).

• Strong experience with device-to-device networking technologies (RDMA, GPUDirect, etc.), including RoCE.

• Experience with zero-copy networking, RDMA verbs and memory registration.

• Familiarity with queue pairs, completions queues, and transport types.

• Strong understanding of operating systems (Linux preferred) and server hardware architectures.

• Ability to analyze complex technical problems and provide effective solutions.

• Excellent communication and collaboration skills.   

• Ability to work independently and as part of a team.

• Experience with version control systems (e.g., Git).   

• Experience with reading and interpreting hardware logs.

Strong candidates will have (Nice to have qualifications)

• Experience with networking technologies like NVLink, Infiniband, ML Pod interconnects.

• Experience with widely deployed Top of Rack Switches (Cisco, Juniper, Arista, etc.)

• Knowledge of server virtualization.

• Experience with tracing tools like perf, eBPF, ftrace, etc.

• Experience with performance testing and benchmarking tools (gProf, vTune, Wireshark, etc.).

• Familiarity with hardware diagnostic tools and techniques 

• Experience with containerization technologies (e.g., Docker, Kubernetes).

• Experience with CI/CD pipelines.

• Experience with Rust.

• Worked on GPU or TPU pods, specifically in the networking domain.

• Understand up-time challenges of very big ML deployments.

• Actively debugged complex network topologies, specifically dealing with cases of node dropouts/failures, route-arounds, and pod resiliency at large.

• Understand performance implications of Pod Networking SW.

Benefits

• Medical, dental, and vision packages with generous premium coverage

  • $500 per month credit for waiving medical benefits

• Housing subsidy of $2k per month for those living within walking distance of the office

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

• Various wellness benefits covering fitness, mental health, and more

• Daily lunch + dinner in our office

• Unlimited compute budget subject to ROI justification

How we’re different

Etched believes in the Bitter Lesson. We are the first inference-focused frontier AI system, betting early on transformer and transformer-like architectures and on increasing model sizes. Our addressable market is the entirety of inference, unlike many of our competitors.

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 and work across disciplines as needed.