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Delos Data

Inference-optimized AI hardware and interconnects

Software Development Engineer in Test - AI

Test Automation EngineeringConfirmed live in the last 24 hours
Full-timeMontreal, QC, CanadaHybrid

About the Job

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 using Docker or Kubernetes and modern CI/CD tools such as GitHub Actions, GitLab CI, or Jenkins.
  • A Bachelor's or Master's degree in Computer Engineering, Computer Science, or a related field.

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 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.
  • Validate the intersection of distributed systems, GPU kernels, and AI frameworks, and build a scalable foundation of trust in the code so performance-critical primitives work reliably at any scale.

Desired Qualifications

  • Experience with Kubernetes, 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.

About the Company

Delos Data

Inference-optimized AI hardware and interconnects

Delos Data builds hardware and infrastructure optimized for AI inference in enterprise data centers, focusing on high‑performance servers and interconnects. It achieves this by engineering how compute is distributed and how memory is interconnected, using chiplet integration and advanced packaging, with architecture, design, and validation of dedicated networking and hardware plus software for real-time visibility. It differentiates itself by pursuing inference-specific needs instead of using components meant for HPC, cloud, or AI training, prioritizing low-latency interconnects and high memory bandwidth for always-on deployments. The goal is to provide reliable, scalable AI inference infrastructure with predictable performance and real-time monitoring at scale.

Company size
11-50
Company stage
Early VC
Total funding
$100M
Headquarters
Palo Alto, CA
Founded
2025

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