Staff ML Engineer
Tasks
- Build and operate ML training experimentation batch inference online inference and edge deployment
- Build edge to cloud feedback loops
- Collaborate on experiment design and evaluation
- Contribute to open-source communities
- Define model packaging validation and deployment workflows for devices
- Design and operate scalable inference systems
- Design end to end ML platform
- Drive developer experience documentation and best practices
- Lead throughput and cost estimation for ML features
- Mentor engineers and applied scientists
- Own end to end delivery for high priority initiatives
- Own reliability observability and security posture
- Partner to design launch and iterate ML powered features
- Provide technical leadership architecture and strategy for ML infrastructure
- Standardize experiment tracking evaluation and regression testing
- Unify training to production workflows
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | AWS | Apache Spark | B testing | Batch inference | CI/CD | Computer Vision | Containers | Distributed Computing | Drift Detection | Experiment tracking | Incident Response | Kubernetes | LLM | Machine Learning | Model Deployment | Model Drift | Model Drift Detection | Model Monitoring | Model Registry | Observability | Offline evaluation | Online Inference | Ray | Regression testing | SLO | Security
Education
N/A
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