Senior Machine Learning Platform Engineer, Foundation AI

About the Role

ML Platform powers hundreds of use cases and billions of inferences per day across Discovery, Safety, Economy, and Creation. We build the primitives that let teams train, evaluate, deploy, and operate models quickly and safely—so a new ML idea can reach production in weeks or less. We’re looking for a Platform Engineer who treats platform as a product: someone who can turn complex ML/AI infrastructure into clear, durable APIs, SDKs, and easy-to-use CLIs/UIs that our internal developers love.

This role blends product thinking, developer experience, backend engineering, and infrastructure at scale.

You Are

• Experienced: 7+ years of professional experience and a wealth of system design experience to build a scalable, reliable ML platform for all of Roblox.
• A Code Machine: Passionate about designing, communicating ideas, and shipping product; driven to get platform features into customers' hands based on user feedback.
• API-Focused: Proficient in API design and developer experience—gRPC/REST APIs, SDKs, CLIs, and simple UIs that developers love to use.
• ML Knowledgeable (Plus): Experience with end-to-end ML model lifecycle management (serving, training, model CI/CD, and GPU resource management) is a plus.
• Automation-Driven: Passionate about infrastructure-as-code and automating painful manual processes to avoid leaving on-call teams burdened by design choices.
• Developer-Centric: Passionate about supporting ML engineers, understanding their needs, and translating them into clean, durable platform abstractions.
• A Clear Communicator: Excels at both written and verbal communication across varying levels of technical detail.
• Educated: Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a similar technical field.

You Will

• Own platform as a product and set direction end-to-end: define requirements, write RFDs, and ship APIs, SDKs, CLIs, and UIs.
• Bootstrap and maintain core ML Platform components: Serving Layer, Model Registry, Pipeline Orchestrator, and Training/Inference control planes.
• Set technical strategy and oversee development of high-scale and reliable infrastructure systems with clear SLOs for latency, availability, and cost.
• Design developer experiences with paved-road templates, golden paths, opinionated defaults, and clear docs to reduce time-to-first-production.
• Instrument the platform to measure adoption, friction, reliability, and cost; use data to prioritize roadmap and validate outcomes.
• Partner across organizations (ML Engineering, Data Science, Infra/SRE, Security, Finance) to optimize performance, safety, and spend, especially for GPU-intensive training and high-QPS inference.
• Propose and implement new platform tooling to improve time to production for MLEs across the full ML lifecycle.
• Stay abreast of industry trends in machine learning and infrastructure.
• Mentor junior and senior engineers, lead design reviews, and drive cross-team architectural decisions that last.

Work Arrangement

Roles based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday.