Hybrid ML Platform Engineer

at Salesforce

• Job title: ML Platform Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $162,800 - $233,900 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, AWS, Cloud, Docker, Grafana, Kubernetes, Python, Terraform
• Location requirements: Hybrid, New York City, United States

About the role

• AI Platform Engineer developing and scaling Salesforce's ML/AI infrastructure, collaborating with teams to drive machine learning innovations. Expertise in cloud technologies is essential.

Responsibilities

• Infrastructure Development: Design, implement, and manage secure and scalable cloud infrastructure (primarily AWS) including networking, permissions management, data management, and kubernetes.
• ML Platform Services: Develop and maintain core ML platform components such as Model Registry, permissions services for project access, and tools for SageMaker default setup and deployments.
• CI/CD and Workflow Automation: Build and optimize CI/CD pipelines using GitHub Actions for efficient and secure code deployment, Docker and package building, and security scanning.
• Networking: Ensure robust and secure connectivity for the platform, including ingress (public and VPN), egress, and domain management (Route53). Manage service mesh (Istio) for traffic routing and security trust between micro services.
• Tooling and Automation: Implement and manage essential tooling to enhance developer productivity and platform security, including secrets management, package/dependency management, testing frameworks, developer self-service tools, automation scripts/bots, and observability integrations.
• Monitoring and Reliability: Contribute to establishing monitoring solutions (e.g., Grafana, PagerDuty) and integrate security scanning to ensure platform health and security.
• Security & Compliance: Participate in security reviews and ensure all platform components adhere to security best practices and compliance requirements.
• Collaboration: Work closely with cross-functional teams, including ML engineers, data scientists, and product managers, to deliver robust and high-performance solutions.
• Documentation: Create and maintain comprehensive documentation for infrastructure, services, workflows, and user guides.

Requirements

• Proven experience as a Platform Engineer, Software Engineer, or ML Infrastructure Engineer.
• Strong software engineering skills, particularly with Python, for building scalable tools, automation scripts, and platform components.
• Strong expertise in cloud platforms, particularly AWS (IAM, EKS, S3, SageMaker, etc.).
• Extensive experience with CI/CD tools, especially GitHub Actions and ArgoCD.
• Proficiency in infrastructure-as-code principles and tools (e.g., Terraform).
• Experience with containerization technologies (Docker) and orchestration (Kubernetes).
• Understanding of networking concepts within cloud environments and service mesh technologies (eg., Istio).
• Experience with MLOps concepts and tools.
• Knowledge of Airflow or other workflow orchestration tools.
• Experience with monitoring and alerting systems (Grafana, PagerDuty).
• Familiarity with Okta or similar identity and access management systems.
• Experience with tenant and project onboarding processes in a multi-tenant environment.
• Familiarity with security best practices and conducting security reviews.
• Ability to manage multiple priorities and dependencies effectively.
• Excellent problem-solving and communication skills.

Benefits

• time off programs
• medical
• dental
• vision
• mental health support
• paid parental leave
• life and disability insurance
• 401(k)
• employee stock purchasing program