Senior Software Engineer; ML Infrastructure, Asset Safety

• Safety and Civility is Roblox’s #1 priority, and our mission is to protect our global community. As a Senior Software Engineer on the Asset Safety team, you will serve as a hands-on technical leader building resilient, highly scalable systems to ensure millions of daily user-generated assets remain safe and civil
• You will tackle complex engineering challenges across diverse modalities—including 3D content, code, images, and video—operating at platform-wide scale across billions of engagement hours while maintaining efficient, low-frequency high-throughput processing
• Architect, build, and optimize end-to-end ML-based safety moderation systems to detect and mitigate problematic content using scalable distributed architectures and event-driven, asynchronous workflows
• Deliver production-grade, AI-driven safety products end-to-end in a dynamic environment, driving execution from initial ideation to deployment while aligning key cross-functional stakeholders
• Drive technical roadmap decisions, implement robust observability and reliability practices, integrate strict security and compliance standards, and enforce rigorous testing and CI/CD pipelines while mentoring engineering team members
• Partner closely with Moderation Operations, Product Management, Data Science, and partner engineering teams across Roblox to deliver cohesive safety solutions

Deep backend engineering expertise in high-capacity microservices, advanced distributed architecture, scalable data storage and management, and event-driven asynchronous processing

Hands-on experience with computer vision techniques and multi-modal architectures spanning image, audio, video, and 3D content modeling

Experience fine-tuning large Vision-Language Models (VLMs) is a plus Proven technical leadership with a track record of architecting, building, and deploying low-latency, distributed ML services to production end-to-end5+ years of professional software engineering experience designing, building, and deploying large-scale ML infrastructure and distributed production systems

Practical experience with streaming and messaging platforms (e.g., Apache Kafka, Apache Flink), end-to-end testing, CI/CD pipelines, security compliance, and comprehensive system observability

Resourceful:
Adaptable in fast-moving environments, with a track record of driving solutions through complex challenges

Analytical:
Driven to investigate large, complex, and ambiguous technical challenges, using data to quantify problem scope and measure solution effectiveness

Team-Oriented:
Dedicated to elevating engineering peers, serving as an active mentor, and cultivating a high-standard culture of engineering excellence

User-Oriented:
Committed to prioritizing the needs of platform users and internal technical partners when designing and scaling backend architecture

Mission-Oriented:
Focused on core organizational goals, bringing strategic foresight and adaptability to balance long-term technical vision with immediate business impact