Staff Software Engineer, ML Infrastructure
Tasks
- Build feedback loops between cloud inference edge devices and data pipelines
- Define SLOs and observability standards for ML services
- Design and evolve real time cloud inference systems
- Drive architecture decisions for Kubernetes based ML platform
- Establish best practices for model lifecycle management and on call
- Identify and remove bottlenecks in ML deployment infrastructure
- Improve throughput latency and cost for production CV models
- Lead incident response and postmortems for ML systems
- Lead technical reviews for system design and capacity planning
- Mentor engineers through design reviews code reviews pairing and guidance
- Set technical direction for ML infrastructure
- Shape production LLM serving patterns and evaluation pipelines
- Write documentation runbooks and architecture decision records
Perks/Benefits
- Employee resource groups
- Free home security system
- Hybrid work model
- Inclusive safe work environment
- Professional home monitoring
- Wellness support
Skills/Tech-stack
AWS EKS | AWS IAM | Amazon S3 | Amazon Web Services | Autoscaling | Batching | C plus plus | CI/CD | Caching | Capacity Planning | Distributed Systems | GPU scheduling | GPU-based inference | Go | High Throughput | High-Throughput Systems | Incident Response | Infrastructure as Code | KServe | KV cache | Kafka | Kubernetes | LLM serving | Load Balancing | Low Latency | Low-Latency Systems | Multi-tenancy | NVIDIA Triton | Observability | Postmortems | Python | Queuing | Ray | Rust | SLOs | Terraform | VLLM | Web Services | “as-code”
Education
N/A
Roles
Regions
Countries
States
Cities
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