Distinguished Engineer, Machine Learning Systems – Economy

About the role

A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.

Responsibilities

• Set the technical direction for ML systems powering core economy experiences: personalization, ranking, generative modeling, and more
• Build and evolve infrastructure for training, serving, and evaluating both traditional ML models and Generative AI models (e.g., embedding-based retrieval, transformers, LLM-based workflows)
• Partner with GenAI teams to explore multi-modal embeddings, avatar generation, and retrieval-augmented generation (RAG) in economic surfaces
• Optimize ML system performance: low-latency serving, efficient GPU training, and cost-aware inference strategies
• Guide the development of robust ML pipelines, online feature stores, and experimentation platforms that support both predictive and generative use cases
• Collaborate with EMs, tech leads, and product partners to align technical architecture with business priorities
• Mentor senior engineers and help establish a strong technical culture across the Economy ML team
• Ensure robustness, observability, and scalability of ML systems that power millions of daily economic interactions
• Own the architecture behind some of Roblox’s most foundational ML systems
• Work across groups at Roblox to advance the state of ML/AI at the company
• Solve real-world problems with real user impact, powering millions of personalized, intelligent economic interactions every day

Requirements

• 10+ years of experience in software engineering or ML infrastructure, with a strong focus on large-scale ML systems
• Deep expertise in building recommendation systems, ranking infrastructure, or real-time personalization engines
• Hands-on experience with Generative AI, such as transformer-based models, LLMs, embeddings, or RAG pipelines
• Proven experience with distributed training, model deployment, and GPU-accelerated workflows
• Strong understanding of ML system architecture: data pipelines, feature stores, inference optimization, experimentation tooling
• Systems-first mindset with strong instincts around scale, cost, performance, and reliability
• Track record of leading large technical initiatives and mentoring senior engineers
• BS, MS, or PhD in Computer Science, Machine Learning, or a related field

Qualifications

• BS, MS, or PhD in Computer Science, Machine Learning, or a related field

Skills

• Software Engineering
• Machine Learning Infrastructure
• Large-Scale ML Systems
• Recommendation Systems
• Ranking Infrastructure
• Real-Time Personalization Engines
• Generative AI
• Transformer-Based Models
• LLMs
• Embeddings
• RAG Pipelines
• Distributed Training
• Model Deployment
• GPU-Accelerated Workflows
• ML System Architecture
• Data Pipelines
• Feature Stores
• Inference Optimization
• Experimentation Tooling

Benefits

• Full-time employment
• Equity compensation
• Robust benefits package

Pay

The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range.

Annual Salary Range $397,460—$455,720 USD

Schedule

Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).