Senior Machine Learning Infrastructure Engineer, Matchmaking

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.

• 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.
• 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.

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