Senior Machine Learning Engineer - Economy ML

The Economy ML team is building the machine learning backbone for Roblox’s virtual economy—from intelligent pricing and personalized storefronts to dynamic layout optimization and avatar content understanding. We partner closely with Marketplace, Payments, and Developer Monetization to deliver ML systems that drive long‑term value across engagement, retention, and creator earnings.

As a Senior Machine Learning Engineer on the team, you will build models that power ranking, personalization, pricing, and optimization across Roblox’s economic surfaces, including Marketplace, Developer Monetization, and Payments. Your work will directly shape how users discover virtual items, how creators earn, and how the platform grows sustainably.

You Will:

• Design and iterate on modern retrieval, ranking, and generative recommendation models (e.g., transformer-based sequential models over user behavior) that personalize item and widget recommendations across the Marketplace and other shopping experiences.
• Make these models efficient to train and serve at scale: choose architectures and modeling approaches with compute, latency, and cost budgets in mind, and partner with ML infrastructure engineers on distributed training and production serving.
• Work backwards from ambiguous product problems and opportunities to design and ship end‑to‑end ML solutions across Economy surfaces, owning the full lifecycle: data and feature exploration, modeling, evaluation, and production deployment on Roblox’s ML platform.
• Develop models and policies that optimize pricing, conversion, and revenue while balancing ecosystem health, payer experience, and creator success.
• Design and run robust experiments (A/B tests and multi‑cell experiments), tying your work to core metrics such as engagement, bookings, and creator earnings.
• Keep models healthy in production: set up monitoring for data and prediction quality, catch drift and regressions early, and help define the team’s on-call practices for ML services.
• Partner cross‑functionally with Product, Backend, Data Science, Design, and other Marketplace / Economy teams to define roadmaps, requirements, and success metrics for ML‑driven features.
• Contribute to the technical direction of the Economy ML team: help shape our modeling roadmap, experimentation strategy, and approaches to long‑term reward optimization across the economy.
• Mentor other engineers through design reviews, code reviews, and hands-on guidance, helping raise the technical bar of the broader Economy organization.

You Have:

• 3+ years of industry experience building recommendation, search, or ranking systems for a high-scale product. Experience with pricing or content understanding is a plus.
• Deep, hands-on expertise in modern recommender architectures (e.g., sequence models of user behavior, multi-task ranking, large sparse embeddings) and in making them efficient to train and serve: comfortable with multi-GPU training and with the tradeoffs between model quality, latency, and cost. Experience applying LLM or multimodal techniques to recommendation is a plus.
• Fluent in the ML lifecycle: Comfortable taking a problem from messy data and product ambiguity through modeling, evaluation, and stable production deployment, with strong coding skills in Python and PyTorch.
• You think in terms of impact on users, creators, and the business, and you can reason clearly about how model choices affect metrics like LTV, engagement, conversion, and creator earnings.
• You are comfortable designing experiments, diagnosing metric movements, and iterating quickly while maintaining statistical rigor and healthy guardrails for the ecosystem.
• You enjoy working closely with engineers, product managers, data scientists, and designers across Marketplace, Payments, and Developer Monetization, and you can drive alignment in complex, multi‑stakeholder environments.
• You take a long‑term view of the Roblox economy, are comfortable challenging assumptions, and can balance quick wins with foundational investments in data, infrastructure, and modeling quality.