Own the transition of developed MLOps pipeline components into an automated pipeline to produce and evaluate models for production
Develop metrics and methodologies to identify algorithmic and training gaps, ensuring the effectiveness of the production models are visible to all stakeholders
Collaborate with cross disciplinary engineering teams to integrate developed algorithms and models into production
Be a hands-on technical contributor to unblock or bootstrap key initiatives
Travel up to 20% of the time to meet with internal and external stakeholders
Requirements
4+ years working with ML researchers to take AI/ML products from research to production
2+ years of building CI/CD pipelines or using Infrastructure-as-code tools
2+ years developing server-side architecture and data models to support data products that integrate machine learning
2+ years with model versioning and experimentation platform like Weights and Biases
Experience deploying code to cloud platform (AWS, Google Cloud Platform, or Azure)
Experience developing AI/ML models in physical or applied science fields OR experience as a signal processing engineer, RF Sytems Engineer, or similar discipline
Benefits
Health, dental, and vision insurance
Unlimited PTO including competitive vacation and holiday schedules
Lifestyle stipends - Monthly mental health, wellness & fitness stipend, in-home office setup stipend and family planning assistance
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