Role Overview
Design, deploy, operate, and troubleshoot secure distributed AI infrastructure in a reliable and scalable way, including multi-node, multi-GPU training and reinforcement-learning environments. Build fleet-level automation and infrastructure-as-code for remediation, repeatable configuration, and drift detection. Turn recurring findings into durable engineering fixes that prevent recurrence.
Work independently from ambiguous research and infrastructure requirements to develop technical designs and production-ready solutions. Partner closely with stakeholders to understand their needs, clarify requirements, and prioritize the highest-impact solutions Drive alignment across engineers, researchers, and partner teams on infrastructure architecture, security and reliability tradeoffs, dependencies, and delivery. Build and improve automation that enables rapid deployment and operation of AI models and supporting infrastructure Build solid relationships with peers through design and code reviews, technical collaboration, and mentoring Embody our Culture and Values Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. - Citizenship & Citizenship Verification: Experience designing, deploying, and operating cloud infrastructure, including distributed, multi-node, multi-GPU training environments.
Experience developing software and automation for production systems. Experience deploying and operating AI infrastructure in Azure, including GPU-backed compute, networking, model serving, and platforms such as Azure AI Foundry or Azure Machine Learning. Experience integrating or operating RL training stacks such as SkyRL, verl, or OpenRLHF and agent environments or sandbox tooling such as Harbor.
Experience with containers, infrastructure-as-code, CI/CD, monitoring, and observability. Experience diagnosing GPU, interconnect, or performance issues in GPU or high-performance computing (HPC) infrastructure. Proficiency writing clean, efficient, and maintainable Python code.
Understanding of LLM inference, model deployment, and the performance, capacity, and reliability requirements of large AI models. Experience driving technical projects from ambiguous requirements to production, including stakeholder alignment and architectural decisions. Experience in cybersecurity, AI security, or red teaming.