Systems Software Engineer, AI Infrastructure

Job Description

What You Will Be Doing:

Develop and maintain large-scale systems supporting critical use-cases including frontier model training for AI Infrastructure, driving reliability, operability, and scalability across global public and private clouds.
Collaborate on tooling for HPC, GPU Training, and AI Model training workflows.
Build tools and frameworks to improve observability, define actionable reliability metrics, and enable fast issue resolution, driving continuous improvement in system performance.
Establish frameworks for operational maturity, lead sustainable incident response protocols, and conduct blameless postmortems to improve team efficiency and system resilience.
Implement SRE fundamentals, including incident management, monitoring, and performance optimization, while designing automation tools to reduce manual processes and operational overhead.
Work with engineering teams to deliver innovative solutions, uphold high standards for code and infrastructure, and contribute to hiring for a diverse, high-performing team.

What We Need to See:

Degree in Computer Science or related field, or equivalent experience with 5+ years in Software Development, SRE, or Production Engineering.
Proficiency in Python and at least one other language (C/C++, Go, Perl, Ruby).
Expertise in systems engineering within Linux or Windows environments and cloud platforms (AWS, Azure, GCP, or OCI).
Strong understanding of SRE principles, including error budgets, SLOs, SLAs, and Infrastructure as Code tools (e.g., Terraform CDK).
Hands-on experience with observability platforms (e.g., ELK, Prometheus, Loki) and CI/CD systems (e.g., GitLab).
Strong communication skills with the ability to convey technical concepts effectively to diverse audiences.
Commitment to fostering a culture of diversity, curiosity, and continuous improvement.

Ways to stand out from the crowd:

Experience in AI training, inferencing, and data infrastructure services.
Proficiency in deep learning frameworks like PyTorch, TensorFlow, JAX, and Ray.
A strong background in cloud or hardware health monitoring and system reliability.
Hands-on expertise in operating and scaling distributed systems with stringent SLAs, ensuring high availability and performance.
Knowledge of incident, change, and problem management processes, fostering continuous improvement in sophisticated environments.