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python kubernetes pytorch jax gpu📋 Description
- Build and scale ML HPC infra: Kubernetes GPU/TPU clusters across clouds.
- Optimize AI/ML training: cost, reliability, performance; RDMA/NCCL/interconnects.
- Troubleshoot bottlenecks and failures to minimize disruption.
- Enable researchers with self-service tools to monitor, debug, and optimize training jobs.
- Drive innovation in ML infrastructure with JAX, PyTorch, and distributed training.
- On-call rotation (24x7) with compensation.
🎯 Requirements
- Deep ML/HPC infra expertise: GPU/TPU clusters and distributed training.
- Kubernetes at scale: deploy/manage cloud-native clusters for AI workloads.
- Strong programming: Python for ML tooling and Go for systems; open-source favored.
- Linux internals, RDMA networking, HPC performance tuning.
- Research collaboration experience with AI researchers/ML engineers.
- Self-directed problem solving and driving impact in fast-paced environments.
🎁 Benefits
- Open and inclusive culture and work environment.
- Work with cutting-edge AI research.
- Weekly lunch stipend, in-office lunches and snacks.
- Health and dental benefits, mental health budget.
- Parental leave top-up up to 6 months.
- Remote-flexible with offices in multiple cities and coworking stipend; 6 weeks vacation.
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