Software Engineer, Systems ML Engineering
Tasks
- Architect ML systems stack initiatives
- Build dashboards alerting and runbooks
- Build model serving pipelines
- Collaborate with machine learning researchers and product engineers
- Conduct code reviews and set coding standards
- Contribute to ML infrastructure roadmap planning
- Coordinate cross team delivery of technical projects
- Define ML infrastructure service level objectives
- Design ML systems infrastructure components
- Develop ML platform tooling
- Drive engineering standards testing and rollout
- Evaluate performance reliability trade offs
- Identify performance bottlenecks in distributed ML
- Implement distributed training frameworks
- Mentor engineers on ML systems best practices
- Translate model requirements into production systems
- Use instrumentation profiling and benchmarking
Perks/Benefits
- N/A
Skills/Tech-stack
Alerting | Benchmarking | C++ | CUDA | Data parallelism | Distributed Training | Experiment tracking | Feature Flagging | Feature Store | GPU Computing | Inference Serving | Instrumentation | Model Parallelism | Model Registry | Model Serving | Pipeline parallelism | Profiling | PyTorch | Python | Runbooks
Education
N/A
Roles
Regions
Countries
States
Cities
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