Principal Machine Learning Platform Engineer

Who you are

• Have 10+ years of professional experience and have a wealth of system design experience upon which to draw to build a scalable, reliable ML platform for all of Roblox
• A Code Machine; you love not only to design and communicate ideas but also to actually ship product. You obsess about user feedback, and constantly drive towards getting platform features in customers hands
• Proficient in API design and developer experience—gRPC/REST APIs, SDKs, CLIs, and simple UIs that developers love to use
• Good to have - experience with the end‑to‑end ML model lifecycle such as model serving, training, model CI/CD, and GPU resources management, and have built ML platform features that are delightful to use
• You're passionate about infrastructure‑as‑code and automating painful manual processes. You push for platform solutions that don’t leave on-call teams carrying the burden of design choices
• Passionate about supporting ML engineers to meet and understand their needs, and translating them into clean, durable platform abstractions
• A clear communicator who excels at both written and verbal communication across differing levels of technical detail
• Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a similar technical field

What the job involves

• We’re looking for a Principal 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. Hands‑on ML experience is a plus; a track record of building internal platforms that developers love is a must
• Own platform as a product and set direction end to end: Define requirements, write RFDs, and ship APIs, SDKs, CLIs, and UIs that make ML@Roblox easy to adopt
• 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 great 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 to ensure the adoption of leading‑edge technologies and practices
• Mentor junior and senior engineers, lead design reviews, and drive cross‑team architectural decisions that last