ML Platform Engineer
Tasks
- Architect model deployment serving and operations across research and product environments
- Build infrastructure and tooling for reliable scalable and cost efficient ML workloads
- Build tools abstractions and agentic systems to reduce operational overhead
- Collaborate with researchers and product engineers to identify pain points
- Design platform systems for model training evaluation and production serving
- Develop internal tools and workflows for human and agent operation
- Improve observability automation reliability and developer experience
- Make architectural tradeoffs to scale platform operations
- Schedule monitor and debug GPU and cloud workloads
Perks/Benefits
- N/A
Skills/Tech-stack
Cloud infrastructure | Datadog | Debugging | Distributed Systems | GPU | GitHub Actions | Kubernetes | Linux | Observability | Performance optimization | Python | Resource allocation | Scheduling | Temporal | Terraform | Workflow Orchestration
Education
N/A
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