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.
Matchmaking is a critical stage downstream of Discovery and Search. Once a player chooses a game to play, matchmaking assigns them to a server for that game. That decision shapes who they play with, the network latency they experience, and the quality of their session. By matching players to servers that fit their needs and each developer’s game, matchmaking helps turn discovery into satisfying play, repeat visits, and long-term engagement, making it a direct lever for Roblox’s growth.
As a Senior Machine Learning Infrastructure Engineer, you’ll build scalable, reliable, and high-performance infrastructure that powers ML systems across our organization. You’ll operate at the scales of tens of billions of scorings, and redefine how we match hundreds of millions of users to millions of games.
You will:
- Work at the intersection of model architecture and ML infrastructure, partnering closely with core modelers, data and AI infrastructure engineers, and product teams to push the boundaries of large-scale training and serving.
- Design and own large-scale data engineering and streaming systems that power ML, including real-time event ingestion, streaming feature computation, training-data generation, and data quality and freshness guarantees. This includes building low-latency pipelines with technologies such as Kafka, Flink, Spark Structured Streaming, and Ray, and keeping online and offline features consistent.
- Build online and continual learning systems that let models adapt to user behavior and content shifts in near real time. This includes incremental and streaming training, online model updates and fast model refresh, embedding and parameter-server synchronization, delayed-feedback and label-joining pipelines, and safe deployment through monitoring, drift detection, and automated rollback.
- Investigate model, data, and systems tradeoffs end to end, from streaming data pipelines and distributed training to low-latency inference and production serving. This includes designing efficient cache strategies, applying pruning and quantization, optimizing GPU utilization and memory efficiency, and developing custom kernels where needed.
- Own the architecture, establish engineering best practices of scalability, reliability, and cost-effectiveness of ML infrastructure (e.g., training, serving, feature). You have deep production experience with low-latency key-value stores and feature registries like Redis, Feast, Tecton, or Cassandra.
- Work closely with data scientists, ML engineers, platform teams, and product stakeholders to design, implement, and operate robust ML platforms that accelerate model development and deployment.
- Stay abreast of industry trends in machine learning and infrastructure to ensure the adoption of leading-edge technologies and practices.
You have:
- 5+ years of experience designing, building, and deploying large-scale machine learning and data systems in production environments.
- 3+ years of experience tech-leading ML infrastructure or data engineering teams.
- Hands-on experience with large-scale streaming and data processing systems (e.g., Kafka, Flink, Spark, Beam) and real-time feature pipelines.
- Experience building or operating online, incremental, or continual learning systems for recommendation, ranking, or search is a strong plus.
- Strong communication skills and a collaborative approach to problem solving.
- BS, MS, or Ph.D. in Computer Science, Engineering, or equivalent experience.
For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page.
Annual Salary Range$243,290—$295,250 USD
Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).
Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Roblox also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process.
For US based roles only, please note the Company may not be able to employ candidates for this role who have United States work authorization related to certain U.S. visa categories, or support future H-1B sponsorship at this time.
