[Remote] Manager Machine Learning Engineering – Amazon Store

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Note: The job is a remote job and is open to candidates in USA. HCA Healthcare is a leading healthcare provider, and they are seeking a Manager of Machine Learning Engineering. The role involves leading the development and implementation of machine learning operations while building and managing a high-performing team of ML engineers to enhance ML delivery and standardization across the organization.

Responsibilities

• Build and lead high-performing team of ML Engineers, including hiring, coaching, and providing career development while fostering a culture of technical excellence and knowledge sharing.
• Actively contribute to MLOps assigned pod development through hands-on coding and technical implementation, leading by example through direct contribution to critical platform components and infrastructure when possible while ensuring pod priorities and technical challenges are addressed.
• Partner with Pod Tech Leads to ensure embedded engineers are delivering according to best practices and are following pod and platform standards.
• Collaborate with AI Platform leadership to inform technical standards and architectural patterns for AI platform, engineering best practices and standardization across pods.
• Drive platform adoption, engineering best practices and standardization across pods.
• Collaborate with other engineering leaders to implement and maintain technical standards while aligning teams around technical decisions.
• Conduct code reviews and participate in technical discussions to maintain high engineering standards and quality of platform usage and MLOps best practices across pods.
• Champion ML platform capabilities across product teams while identifying and addressing barriers to platform adoption, ensuring consistent implementation of platform patterns.
• Balance hands-on development with technical leadership and team management responsibilities to ensure successful and consistent platform usage.
• Collect and synthesize feedback from engineers and pod tech leads to inform platform priorities and roadmap based on pod needs.
• Create opportunities for technical growth and skills development for team members.
• Lead technical discussion and architecture review to promote platform-aligned solutions.

Skills

• Bachelor's degree - Required
• 1+ year(s) of experience in a leadership role - Required
• 7+ years of experience in software engineering with a focus on ML Engineering and AI - Required
• Current hands-on coding abilities with recent experience in ML/AI systems - Required
• Strong technical background in ML engineering with demonstrated coding expertise - Required
• Deep Python development expertise with a focus on ML systems - Required
• Strong understanding of Machine Learning workflows and operational requirements through direct implementation - Required
• Hands on experience implementing and scaling model CI/CD and Continuous Training pipelines - Required
• Hands on experience with modern Python development practices including typing, linting, test frameworks, and package management - Required
• Deep understanding of containerization and deployment of ML/AI models and systems - Required
• Strong background agile development within product teams - Required
• Understanding of ML Development Lifecycle management and MLOps best practices - Required
• Experience managing team of embedded engineers distributed across product teams - Required
• Understanding of ML Monitoring and observability - Required
• Master's degree - Preferred
• Engineering or platform engineering - Preferred
• Experience with LLMs and Infrastructure - Preferred
• Experience integrating with feature stores - Preferred
• Deep understanding of ML/AI system unit testing, integration testing, and load testing - Preferred
• Experience with Distributed model training - Preferred
• Hands on experience with Kubeflow, Argo, MLFlow or other ML/AI Training orchestrators - Preferred
• Hands on experience and knowledge of ML/AI metadata tools - Preferred
• Deep hands-on experience with Terraform or other IaC tools - Preferred

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