Senior Software Engineer (Machine Learning) - Economy ML

Roblox•San Mateo, CA

5d•$243,290 - $295,250•Onsite

About The Position

The Economy ML team builds the machine learning backbone for Roblox’s virtual economy, including intelligent pricing, personalized storefronts, dynamic layout optimization, and avatar content understanding. The team partners with Marketplace, Payments, and Developer Monetization to deliver ML systems that support engagement, retention, and creator earnings. As a Senior ML Software Engineer, you will build infrastructure and backend services to train, serve, and integrate models for ranking, personalization, and pricing across Roblox’s economic surfaces. You will help move models from ideas into production and improve their performance at scale. This includes building training and data pipelines, model-serving systems and APIs, production deployment workflows, and reliable, observable ML services. You will also contribute to the team’s technical direction and mentor other engineers. For roles based in an office, the schedule is onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday, unless otherwise noted. For roles based at the San Mateo, CA headquarters, the stated annual base salary range is $243,290–$295,250. Full-time employees are eligible for equity compensation and benefits.

Requirements

At least 5 years of software engineering experience, including at least 2 years building and operating ML infrastructure or ML-backed production services at high scale.
Strong engineering fundamentals in Python and at least one other backend language.
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.
Experience profiling and optimizing systems for utilization, memory, latency, and cost.
Experience building batch and streaming data pipelines and feature stores, with attention to data quality and training/serving skew.
Ability to take ownership of production health, including monitoring, alerting, and incident response.
Ability to work closely with ML engineers, platform teams, product managers, and data scientists, and drive alignment across stakeholders.
Understanding of how infrastructure choices affect model quality and product metrics, with an ability to prioritize work by impact on users, creators, and the business.
BS, MS, or Ph.D. in Computer Science, Engineering, or a related technical field, or equivalent practical experience.

Nice To Haves

Experience with recommendation, search, or ads systems.
Experience with tools such as Spark, Flink, and Kafka.

Responsibilities

Build and operate distributed training infrastructure, efficient data loading, and reproducible pipelines for Economy ML ranking, recommendation, and pricing models.
Work with ML engineers to profile and optimize model training and inference, including GPU utilization, memory, throughput, and latency.
Design and build backend services and APIs that integrate model predictions into Marketplace, Payments, and Developer Monetization, with defined latency budgets and graceful fallbacks.
Build and scale batch and real-time data and feature pipelines, prioritizing freshness, correctness, and training/serving consistency.
Own the path from trained model to production serving, including packaging, deployment, versioning, shadow and canary rollouts, and integration with A/B experiments.
Establish reliability and observability practices for owned systems, including logging, monitoring, alerting, and on-call.
Partner with central ML platform and data infrastructure teams and with Marketplace and Economy backend teams; adopt shared infrastructure and contribute improvements.
Help shape the team’s technical direction, training and serving stack, infrastructure roadmap, and tradeoffs among model quality, latency, and cost.
Mentor engineers through design reviews, code reviews, and hands-on guidance.