Distinguished Engineer, Machine Learning Systems – Economy

Roblox Roblox · Consumer · San Mateo, CA · Machine Learning

Distinguished Engineer/Technical Director to lead ML systems strategy and technical direction, focusing on large-scale recommendations, infrastructure, and Generative AI applications for Roblox's economy. The role involves building systems for retrieval, ranking, generative modeling, and LLM-powered personalization at massive scale, requiring deep systems thinking and hands-on ML expertise.

What you'd actually do

  1. Set the technical direction for ML systems powering core economy experiences: personalization, ranking, generative modeling, and more
  2. 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)
  3. Partner with GenAI teams to explore multi-modal embeddings, avatar generation, and retrieval-augmented generation (RAG) in economic surfaces
  4. Lead efforts to optimize ML system performance: low-latency serving, efficient GPU training, and cost-aware inference strategies
  5. Guide the development of robust ML pipelines, online feature stores, and experimentation platforms that support both predictive and generative use cases

Skills

Required

  • software engineering
  • ML 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
  • systems thinking
  • scale
  • cost
  • performance
  • reliability
  • leading technical initiatives
  • mentoring senior engineers

Nice to have

  • 3D content
  • UGC
  • avatar metadata
  • behavioral and economic signals
  • multi-modal embeddings
  • avatar generation

What the JD emphasized

  • AI/ML is a top company priority
  • Real-world scale
  • Full-system ownership
  • Complex surfaces, rich signals
  • Impact meets innovation
  • 10+ years of experience in software engineering or ML infrastructure
  • 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
  • Proven experience with distributed training, model deployment, and GPU-accelerated workflows
  • Strong understanding of ML system architecture
  • Systems-first mindset
  • Track record of leading large technical initiatives and mentoring senior engineers

Other signals

  • large-scale recommendations
  • infrastructure
  • emerging Generative AI applications
  • LLM-powered personalization
  • retrieval, ranking, generative modeling