Soft Engineer - Solution Validation, Benchmarking & Release Engineer

Meet the Team

We are a small, agile, and highly collaborative team at the forefront of AI Infrastructure Automation and Benchmarking Certification. We partner closely with leading hardware and software vendors to design, validate, and deliver curated AI infrastructure solutions to our customers - all built on proven reference architectures that reduce risk and accelerate time-to-value.

Because were a lean team, every member has real ownership and visibility into outcomes - from automating complex infrastructure workflows to running rigorous benchmarking and certification processes that ensure our solutions perform reliably at scale. We move fast, communicate openly, and lean on each others expertise daily, making this a great environment for engineers who want to work across the full stack of AI infrastructure rather than being siloed into one narrow function.

Your Impact

As a Software Engineering Technical Leader, you will own CVIS/NVIS, AI Cluster Validation, end-to-end solution testing, RA certification, GPU debugging, performance profiling, bottleneck analysis, acceptance criteria, test plan and automation, release qualification, and readiness across AI cluster and Cisco network-switch solutions. You will:

Develop test plans, validation matrices, automation, and exit criteria for AI cluster solutions.

Execute CVIS/NVIS, AI Cluster Validation, GPU burn, NCCL, workload, resilience, and interoperability test suites.

Model workloads, profile end-to-end performance, analyze bottlenecks, and produce benchmark reports.

Validate Cisco network-switch, multi-plane, ToR, compute, storage, and software integrations.

Drive defect triage, evidence collection, release qualification, RA certification, and acceptance-report packaging.

Minimum Qualifications

Bachelors + 8 years of related experience, or Masters + 6 years of related or equivalent related work experience.

Experience building and executing end-to-end validation for GPU or distributed AI systems.

Experience with test strategy, automation, Python, Linux, performance analysis, and defect-triage skills.

Prior experience translating system requirements into measurable tests, acceptance criteria, and release gates.

Experience with technical writing and cross-functional communication.

Preferred Qualifications

CVIS/NVIS, NVIDIA GPU platforms, NCCL, Kubernetes, Slurm, network testing, and storage validation.

Benchmark engineering, workload modeling, CI-based testing, observability, and release management.

Experience with Cisco networking and AI cluster reference architectures.

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Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.