Senior GPU Cluster Software Engineer

learn and grow.

What you'll be doing:

• Work in an agile and fast-paced global environment to gather requirements, architect, design, implement, test, deploy, release, and support large scale distributed systems infrastructure with monitoring, logging, visualization, and alerting capabilities with promised uptime

• Build internal profiling tools for real world ML/DL applications running on HPC GPU clusters for failure and efficiency analysis to help improve current and future generation of GPU clusters and associated HWs

• Understand state of the art improvements in ML/DL domain, and work with various application owners and research teams to add / improve profiling needs for current and potential future supported features

What we need to see:

• BS+ in Computer Science or related (or equivalent experience) and 5+ years of software development (in Python)

• Experience with Gitlab (or another source code management) branch/release, CI/CD pipeline, etc.

• Solid understanding of algorithms, data structures, and runtime/space complexity

• Experience working with distributed system software architecture

• Basic understanding of HPC GPU cluster, slurm

• Basic understanding of Machine learning concepts and terminologies

• Background with databases - SQL and NoSQL (prometheus, elasticsearch, opensearch, redis, etc.)

• Experience with distributed Data Pipeline, Telemetry, Visualizations (Kibana, Grafana, etc.), Alerting (pagerduty, etc.)

Ways to stand out from crowd:

• Experience debugging functional and performance issues in HPC GPU clusters
• Background in running and instrumenting distributed LLM training on a multi gpu HPC cluster
• Knowledge of LLM training features and libraries - Checkpointing, Parallelism, Pytorch, Megatron-LM, NCCL
• Experience with HPC schedulers such as Slurm
• Background with Opentelemetry