Software Engineer, GenAI Platform

Jobverse.io; San Mateo (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Roblox is seeking to build and operate platforms and services that support machine learning, generative AI, and agentic experiences across Roblox-scale workloads. The role focuses on improving AI development speed, reliability, observability, and cost efficiency while balancing performance and responsible data use.

The candidate should have experience or capability in ML, generative AI, agentic systems, distributed systems, and GPU infrastructure, with a focus on scaling, quality, latency, and

Qualifications

• Experience or capability in machine learning, generative AI, and agentic systems.
• Experience with distributed systems and GPU infrastructure.
• Ability to balance quality, latency, reliability, and responsible data use at scale.

Responsibilities

• Build and operate platforms and services for ML, generative AI, and agentic experiences.
• Improve AI development speed, reliability, observability, and cost efficiency.
• Scale systems to Roblox-scale workloads while ensuring responsible data use.

Skills

Machine Learning

Generative AI

Agentic Systems

Distributed Systems

GPU Infrastructure

ML Frameworks

Large Language Models

Platform Engineering

Reliability

Observability

Cost Efficiency

Responsible Data Use

Job description

Build and operate platforms and services that support machine learning, generative AI, and agentic experiences across Roblox. Improve AI development speed, reliability, observability, and cost efficiency while contributing to systems at Roblox scale.

Requirements: The role calls for experience or capability in machine learning, generative AI, agentic systems, distributed systems, and GPU infrastructure. It also requires balancing quality, latency, reliability, and responsible data use in large-scale systems.

Key Skills: Machine Learning, Generative AI, Agentic Systems, Distributed Systems, GPU Infrastructure, ML Frameworks, Large Language Models, Platform Engineering, Reliability, Observability, Cost Efficiency, Responsible Data Use