Senior ML Infra Engineer - Matchmaking at Scale

Roblox; San Mateo (CA); On-site; USD 243,000 - 295,000; Full time

Job summary

Roblox is seeking a Senior Machine Learning Infrastructure Engineer to build scalable, reliable ML systems that power matchmaking and recommendations at massive scale. You will shape data platforms, streaming pipelines, and continual learning capabilities, collaborating with data scientists, ML engineers, and platform teams.

The role emphasizes end-to-end ownership of ML infrastructure, from data ingestion to deployment, with a focus on low-latency inference, GPU utilization, and

Qualifications

• 5+ years designing, building and deploying large-scale ML/data systems in production.
• 3+ years leading ML infrastructure or data engineering teams.
• Experience with Kafka, Flink, Spark, Beam and real-time feature pipelines.
• Experience with online, incremental, or continual learning systems for recommendation, ranking, or search is a plus.
• Strong communication and collaborative problem-solving.
• BS, MS, or Ph.D. in CS/Engineering or equivalent.

Responsibilities

• Build scalable, reliable ML infrastructure powering models and data systems.
• Operate at tens of billions of scorings and match scales.
• Design and own large-scale data engineering and streaming systems (real-time ingestion, feature computation, training-data generation).
• Build online and continual learning systems for near real-time adaptability.
• Investigate end-to-end trade-offs from streaming pipelines to low-latency inference.
• Own architecture and engineering best practices for scalable ML infrastructure.

Skills

ML infra

Streaming data

Distributed training

Low-latency systems

Communication skills

Education

BS/MS/PhD in CS/Engineering

Tools

Redis

Feast

Tecton

Cassandra

Job description

Roblox is seeking a Senior Machine Learning Infrastructure Engineer to build scalable, reliable ML systems that power matchmaking and recommendations at massive scale. You will shape data platforms, streaming pipelines, and continual learning capabilities, collaborating with data scientists, ML engineers, and platform teams.

The role emphasizes end-to-end ownership of ML infrastructure, from data ingestion to deployment, with a focus on low-latency inference, GPU utilization, and