About the company
Roblox is building the tools and platform that empower a global community of developers and creators to build immersive experiences. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.
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
- Lead efforts to 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.
- Help scale a fast-growing ML organization and define Roblox’s future economy intelligence stack
- 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
Conditions
- For roles that are based at our headquarters in San Mateo, CA: 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. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page.
- Annual Salary Range: $397,460 — $455,720 USD
- Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).