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Roblox

Senior Software Engineer (Machine Learning) - Economy ML

San Mateo, CA, United States; San Mateo, CAUnited States
Senior · Software Engineering
POSTED 2 NOVVERIFIED 6H AGO
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Pay · posted
$243k–295k
H-1B median $260k
Open for
0d
since 2 Nov
H-1B · FY26
190
4 PERM · UK sponsor
Hiring · 30d
266
+1 net · open roles
PythonPyTorch

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 ML Software Engineer on the team, you will build the infrastructure and backend services that train, serve, and integrate the models powering ranking, personalization, and pricing across Roblox’s economic surfaces, including Marketplace, Developer Monetization, and Payments. Your work determines how quickly we can take new models from idea to production, and how well they perform once they are serving millions of users.

You Will

  • Build and operate the training infrastructure behind Economy ML’s ranking, recommendation, and pricing models: distributed training jobs, efficient data loading, and reproducible pipelines from raw data to trained model.
  • Co-design models and systems with ML engineers: profile and optimize training and inference for our ranking and recommendation models, improving GPU utilization, memory, throughput, and latency (e.g., moving models to GPU serving, request batching, mixed precision, faster request-time feature processing).
  • Design and build the backend services and APIs that bring model predictions into Marketplace, Payments, and Developer Monetization, with clear latency budgets and graceful fallbacks.
  • Build and scale the batch and real-time data and feature pipelines that feed training and serving, with a focus on freshness, correctness, and training/serving consistency.
  • Own the path from trained model to production serving: packaging, deployment, versioning, shadow and canary rollouts, and integration with A/B experiments on Roblox’s ML platform.
  • Make the systems you own reliable and observable, and set the team’s best practices for logging, monitoring, alerting, and on-call across ML services.
  • Partner with Roblox’s central ML platform and data infrastructure teams, and with Marketplace / Economy backend teams, to adopt shared infrastructure and contribute improvements back to it.
  • Contribute to the technical direction of the Economy ML team: help shape our training and serving stack, infrastructure roadmap, and the tradeoffs between model quality, latency, and cost.
  • Mentor other engineers through design reviews, code reviews, and hands-on guidance, helping raise the technical bar of the broader Economy organization.

You Have

  • 5+ years of software engineering experience, including 2+ years building and operating ML infrastructure or ML-backed production services at high scale. Experience with recommendation, search, or ads systems is a strong plus.
  • Solid engineering fundamentals in Python and at least one other backend language, with a track record of designing and operating distributed, low-latency services.
  • Hands-on experience training and serving deep learning models in production with PyTorch, including scaling training across GPUs and nodes and deploying models on GPU inference servers. Comfortable profiling and optimizing for utilization, memory, latency, and cost.
Source · Roblox · GreenhouseFirst seen 2 NovOriginal ↗Method