Machine Learning Engineer (Recommender Systems)
Design, Build and Deploy
- Architect and operationalize end-to-end machine learning pipelines utilizing Databricks and Spark for web-scale data processing.
- Justify critical architectural decisions across complex recommendation frameworks, from Wide & Deep and Two-Tower systems to sequential GRU4Rec models, with first-principles reasoning.
- Deploy high-dimensional user embeddings, hybrid search strategies, and continuous evaluation frameworks that go directly to production.
Operating in a High-Bar Environment
- Work among senior ML engineers who hold elite standards, no hand-holding, full end-to-end pipeline ownership expected.
- Your systems reach real Fortune 500 U.S. clients, algorithmic latency, retrieval precision, and pipeline reliability are your core accountabilities.
- This is production-grade machine learning, not experimental modeling, static prototypes, or basic demos.
Architect the personalization ecosystems that power enterprise-scale product discovery.
Distributed Pipeline Infrastructure
Building and managing large-scale, high-throughput feature engineering and data processing pipelines utilizing PySpark within Databricks platforms.
Advanced Deep Recommendation
Networks Implementing and optimizing sophisticated architectures, including Two-Tower, Transformer-based, and deep sequential models, to capture evolving user intent at scale.
Discovery & Search Optimization
Deploying high-dimensional user embeddings and hybrid search strategies to maximize retrieval precision, semantic product discovery, and user engagement across platforms.
Statistical Rigor & Validation
Designing clean experimental frameworks, continuous drift monitoring, and robust A/B testing pipelines mapped directly to core enterprise business KPIs.
Selection Process
Validate Your Foundation
Submit your application. We prioritize engineers with proven production experience, deep algorithmic skills, and the domain fluency to communicate technical trade-offs clearly in English.
Prove Your Craft
Complete a focused technical assessment, then demonstrate your mastery in live coding, machine learning fundamentals, and scalable system design.
Join the team and build the future of data and AI
Work as a full-time Factored engineer, embedded as a strategic partner in environments where your execution directly transforms real systems and impacts global users.
FAQs
Research | Our team has published at NeurIPS, ICML, NASA and more.
