Principal Machine Learning Engineer, ML Platform
Tasks
- Build ML platform roadmap
- Build batch inference and scoring pipelines
- Conduct design reviews and RFCs
- Create observability for ML systems
- Develop ML lifecycle tooling with MLflow
- Drive ML platform adoption reliability and cost performance
- Establish ML engineering standards for CI CD and incident response
- Evaluate inference frameworks and deployment patterns
- Implement Kubernetes model serving for real time inference
- Mentor engineers on platform engineering
- Optimize inference performance bottlenecks
- Own ML lifecycle architecture decisions
- Partner with data science for reproducible deployment frameworks
- Set production readiness standards
Perks/Benefits
- Charity donation match
- Coaching
- Company offsites
- Dental coverage
- Flexible working hours
- Healthcare coverage
- Learning stipend
- Professional development resources
- Remote-first
- Vision coverage
- Volunteer days off
- WFH stipend
Skills/Tech-stack
Agent workflows | Amazon SageMaker | Artifact management | Autoscaling | Batch inference | CI/CD | CPU | Data Drift | Data Quality | Databricks | Evaluation harnesses | Experiment tracking | Feature Store | GPU | Google Vertex | Google Vertex AI | Incident Response | Kubernetes | LLM Evaluation | LLM monitoring | MLflow | Machine Learning | Model Registry | Model versioning | Observability | Real Time | Real-time Inference | SLO | Serialization | Vertex AI
Education
N/A
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