Software Engineer - AI Research Clusters

In this role, you will propose and implement engineering solutions to ensure delivery of functional, reliable, secure, and performance-optimal GPU clusters to internal researchers, enable them to focus on training and development by reducing operational disruption and overhead, empower them for self-service continuous improvement on reliability, operational excellence & performance. Your work will empower scientists and engineers to train, fine-tune, and deploy the most advanced ML models on some of the world’s most powerful GPU systems.
 

What You'll Be Doing:

• In this position, you will work with coworkers across the AI Platform organization to understand the pain points of validating, monitoring and operating GPU clusters at scale. Then you will design, develop and maintain engineering solutions to solve those pain points systematically.

• You will also research in traditional AIOps and the emerging Agentic AI, and leverage it to further reduce the operation toil.

• You will participate in on-call support for systems, platforms built and owned by the team.

What We Need To See:

• BS/MS in Computer Science, Engineering, or equivalent experience.

• 2+ years in software/platform engineering, including 1 year in ML infrastructure or distributed systems.

• Experience in software development lifecycle on Linux-based platforms.

• Strong coding skills in languages such as Python, C++ or Rust.

• Experience with Docker, Kubernetes, GitLab CI, automated deployments.

• Experience with AIOps or Agentic AI and apply it successfully in production environment.

Ways To Stand Out From The Crowd:

• Proficiency with full-stack development: Relational Data Modeling, DB optimization, REST API Semantics, Javascript, CSS, providing API as a service.

• Passion for building developer-centric platforms with great UX and strong operational reliability.

• Experience running Slurm or custom scheduling frameworks in production ML environments.

• Familiarity with GPU computing, Linux systems internals, and performance tuning at scale.