Jobs / United States / Roblox Corporation

Roblox Corporation

Senior Machine Learning Engineer - Economy ML

San Mateo, CA, United States Posted today

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Skills this job asks for

Machine learningProduct managementA/B testingLLMs

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Sponsorship verdict

Sponsorship possible

One solid signal, not two. Worth applying, and worth asking about sponsorship early.

  • Employer is on a government sponsor recordThe US Department of Labor certified 197 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first.
  • The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
  • No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area.
  • What Roblox Corporation paid sponsored hires in similar roles122 certified filings for “Senior Software Engineer” (Software Developers) in CA: $227k–$295k, median $243k.
  • Confirmed live todayWhen a source last listed this job as open.
Details and sources
Employer is on a government sponsor record
The US Department of Labor certified 197 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. Source: LCA disclosure data (US Department of Labor (OFLC)).
No salary bar for this route
H-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
What Roblox Corporation paid sponsored hires in similar roles
122 certified filings for “Senior Software Engineer” (Software Developers) in CA: $227k–$295k, median $243k. Most were filed at wage level IV (41%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).

US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).

A verdict summarises public evidence. It is not legal advice and never a guarantee: the employer and the immigration authority decide. Sign in to factor in where you can already work.

About the role

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. 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 qua

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