Senior Machine Learning Engineer - Marketplace & Economy

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect across 3D immersive digital experiences created by a global community.

The Economy ML team builds the machine learning backbone for Roblox’s virtual economy—powering intelligent pricing models, personalized Marketplace storefronts, dynamic layout optimization, and avatar content understanding. Partnering closely with Marketplace, Payments, and Developer Monetization teams, the team delivers high-impact ML systems that directly drive user engagement, long-term retention, platform bookings, and creator earnings.

As a Senior Machine Learning Engineer, you will take end-to-end ownership of modern retrieval, ranking, and recommendation models (e.g., transformer-based sequential models over user behavioral sequences). You will optimize model architectures for distributed multi-GPU training and low-latency production serving, build policies for intelligent pricing and conversion, and run rigorous online A/B experiments to scale Roblox's virtual economy sustainably.

Key Responsibilities

• Recommendation & Ranking Architectures: Design, train, and deploy modern retrieval, ranking, and sequential recommendation models (transformer-based user sequence models, multi-task ranking, large sparse embeddings) across Marketplace and shopping surfaces.
• Distributed Training & Serving Efficiency: Optimize deep learning architectures for compute efficiency, latency, and serving costs using Python, PyTorch, and multi-GPU distributed training frameworks.
• End-to-End ML Lifecycle: Own the complete ML pipeline—from messy data exploration and feature engineering through model training, offline evaluation, and production deployment on Roblox’s ML platform.
• Pricing & Revenue Optimization: Formulate models and policies that optimize item pricing, conversion rates, and long-term user LTV while preserving ecosystem health, payer trust, and creator revenue.
• A/B Experimentation & Analytics: Design and execute multi-cell A/B experiments, diagnosing metric movements across bookings, engagement, and creator earnings with statistical rigor.
• Model Health & MLOps: Establish continuous monitoring for data drift, prediction quality, and model degradation; contribute to operational health and team on-call rotations for ML services.
• Cross-Functional Alignment & Mentorship: Partner with Product, Backend, Data Science, and Design leads to define ML roadmaps, and mentor engineers through design and code reviews.

Requirements

Machine Learning & Recommender Systems Depth

• 3+ years of industry experience building, shipping, and scaling recommendation, search, ranking, or pricing systems for high-scale consumer products.
• Recommender Systems Expertise: Deep hands-on proficiency with modern recommender architectures (user behavior sequence models, multi-task learning, large-scale sparse embedding tables) and multi-GPU distributed training using PyTorch and Python.
• Full ML Lifecycle Mastery: Proven track record translating ambiguous product requirements into production-ready ML services—from raw data pipelines to low-latency serving nodes.
• Business & Ecosystem Reasoning: Ability to reason quantitatively about how model decisions impact macro economics, long-term user LTV, retention, conversion, and creator payouts.
• A/B Testing & Statistics: Practical experience designing online experiments, diagnosing metric guardrails, and evaluating model trade-offs under real-world constraints.

Preferred Qualifications

• Experience applying Large Language Models (LLMs) or multimodal deep learning techniques to recommendation and search problems.
• Domain experience in virtual item pricing, digital marketplaces, e-commerce personalization, or avatar content understanding.
• Familiarity with offline-to-online evaluation consistency, feature stores, and automated model monitoring tools.

Total Rewards & Benefits

• Compensation & Equity: Annual base salary range of $260,300 – $312,970 USD + comprehensive equity compensation package.
• Healthcare & Wellness: Comprehensive medical, dental, and vision insurance coverage options for employees and eligible dependents.
• Retirement & Financial: 401(k) retirement savings plan with employer match, life insurance, and flexible spending accounts.
• HQ Campus Perks: On-site catered meals, modern collaborative workspaces, learning and development stipends, and team events.