Sr. ML Platform Engineer (Hybrid)
Tasks
- Build observability solutions
- Conduct post-mortems
- Configure alerting workflows
- Debug memory leaks
- Debug resource contention
- Debug scheduling conflicts
- Develop runbooks
- Diagnose distributed systems issues
- Implement automated health checks
- Improve HPC cluster utilization
- Maintain platform reliability metrics
- Mentor engineers on debugging techniques
- Optimize GPU allocation
- Optimize Ray clusters
- Optimize SLURM job scheduling
- Optimize Spark jobs
- Optimize resource allocation
- Perform root cause analysis
- Profile performance bottlenecks
- Resolve production incidents for inference pipelines
- Resolve production incidents for training pipelines
- Troubleshoot JupyterHub spawner issues
- Troubleshoot kernel crashes
Perks/Benefits
- Employee networks
- On-call support
- Paid adoption leave
- Paid parental leave
- Professional development
- Vacation and holidays
- Volunteer opportunities
- Wellness programs
Skills/Tech-stack
AWS | Airflow | Apache Spark | CUDA | Capacity Planning | Chaos Engineering | Debugging | Distributed tracing | Docker | Google Cloud | Grafana | JupyterHub | Kubeflow | Kubernetes | Linux | Log Aggregation | MLflow | Microsoft Azure | OCI | Observability | Performance Tuning | Profiling | Prometheus | Python | Ray | Slurm | Unix
Education
N/A
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