Senior ML Infra Engineer: Real-Time Scale

Game Companies; San Mateo (CA); In loco; USD 243.000 - 295.000; Tempo pieno

Vantaggi offerti da questo lavoro

Equity compensation

Benefits

Descrizione del lavoro

Roblox is seeking a Senior Machine Learning Infrastructure Engineer to build scalable, reliable, and high-performance ML infrastructure powering our systems at scale. You will collaborate with modelers, data and AI engineers, and product teams to advance large-scale training and serving.

Role focuses on real-time data pipelines, low-latency inference, model updates, and safe deployment with monitoring and rollback capabilities.

Competenze

• 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.

Mansioni

• Work at the intersection of model architecture and ML infrastructure, partnering with modelers, data and AI infrastructure engineers, and product teams.
• Design and own large-scale data engineering and streaming systems powering ML, including real-time event ingestion and feature computation.
• Build online and continual learning systems for near real-time adaptation and safe deployment.

Conoscenze

ML infrastructure

Data systems design

Streaming data

Low-latency systems

Team leadership

Communication

Formazione

BS/MS/PhD in CS/Engineering

Strumenti

Kafka

Flink

Spark

Beam

Redis

Tecton

Cassandra

Ray

Descrizione del lavoro

Roblox is seeking a Senior Machine Learning Infrastructure Engineer to build scalable, reliable, and high-performance ML infrastructure powering our systems at scale. You will collaborate with modelers, data and AI engineers, and product teams to advance large-scale training and serving.

Role focuses on real-time data pipelines, low-latency inference, model updates, and safe deployment with monitoring and rollback capabilities.