Software Development Engineer in Test - AI

Delos Data•Charlottesville, VA

2d•$140,000 - $200,000•Hybrid

About The Position

We are a stealth-mode startup building foundational technology to address performance, scalability, and resiliency challenges in large-scale AI data center clusters. We are backed by top-tier VC firms and notable angel investors. The company is led by experienced builders and operators who have founded companies, taken them to scale, and exited successfully. We work with a strong sense of unity and shared responsibility, and we expect trust, integrity, and respect in how we collaborate and make decisions. We hold ourselves accountable to one another and to the quality of the work we deliver. Headquartered in Silicon Valley, we operate across a mix of remote and on-site locations in the U.S. and Canada. We aim to create an environment where people are treated fairly, supported in their growth, and are empowered to do meaningful work alongside others who take the craft seriously.

Requirements

Strong proficiency in Python (for automation and orchestration).
Proven experience building or extending test automation frameworks for complex back-end systems.
Proven ability to troubleshoot automated test failures within complex large-scale distributed systems, and identify root causes.
Experience with containerization (Docker/Kubernetes) and modern CI/CD tools (GitHub Actions, GitLab CI, or Jenkins).

Nice To Haves

Experience with Kubernetes or Terraform or Ansible for managing test-bed environments.
Experience testing high-performance networking protocols or distributed file systems.
Experience testing software that interacts directly with drivers or firmware.

Responsibilities

Design, develop, and maintain a robust automated testing framework from the ground up that supports distributed AI training and inference workloads.
Develop complex test plans that go beyond unit tests, focusing on end-to-end system integration, stress testing, and hardware-software boundary conditions.
Partner closely with System Engineers to debug deep-seated issues in distributed clusters, using telemetry and profiling tools to identify bottlenecks.