Senior Platform/MLOps Engineer

🕒 May 13

🇺🇸 United States – Remote

💵 $150k - $170k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Bright Machines

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Headquartered in San Francisco, Bright Machines is an industry leading technology company that offers an innovative approach to intelligent, software-defined manufacturing. It leverages computer vision, machine learning, 3D simulation, and adaptive robotics to fundamentally change the flexibility, scalability, and economics of production. With more than 500 employees worldwide, Bright Machines operates R&D centers in the U.S. and Israel, with additional field operations in the U.S., Mexico, China, and Poland. In 2019 Bright Machines was named a “Technology Pioneer” by the World Economic Forum and one of “America’s Most Promising Artificial Intelligence Companies” by Forbes.

📋 Description

• Design, implement, and maintain reliable, scalable, and secure infrastructure, applications, and tooling, with a focus on our ML/AI pipelines and workloads • Write clean, maintainable code, and perform peer code-reviews • Write clear and concise documentation and engage in cross-team communication and knowledge sharing • Work with other team members to investigate design approaches, prototype new technology and evaluate technical feasibility • Pair with adjacent teams to understand how your frameworks and infrastructure are actually used in the field, continuously improving them and leveraging recent advances to improve developer velocity

🎯 Requirements

• At least 5+ years of experience in Platform Engineering, DevOps, or Site Reliability Engineering (SRE). • B.S. or M.S. degree (or equivalent) in Computer Science, Engineering, or a related field • Proficiency in at least one modern programming languages (Python, Javascript, C#, Go, etc) • Demonstrated industry best-practices in MLOps • Proficiency with CI/CD tools and GitOps workflows • Familiarity with running GPU workloads in kubernetes • Strong knowledge of Kubernetes (self-hosted and managed) and modern k8s paradigms (e.g. CNCF) • Proficiency with Infrastructure as Code tools (Terraform, etc) and configuration management tools (Ansible, etc) • Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry)