Member of Technical Staff – Applied ML, RecSys

Role Overview

• Act as the technical owner for enterprise customer engagements involving recommendation and ranking workloads
• Translate customer requirements into concrete specifications for recommendation models
• Design and execute data pipelines for user interaction data, feature engineering, and training data curation at scale
• Fine-tune and adapt large-scale sequential recommendation models (e.g., HSTU-style architectures) for customer-specific use cases
• Design task-specific evaluations for recommendation model performance (ranking quality, latency, throughput) and interpret results
• Build reusable applied tooling and workflows that accelerate future customer engagements

Requirements

• Hands-on experience building or fine-tuning recommendation models at scale (not just off-the-shelf collaborative filtering)
• Experience with sequential recommendation architectures, user behavior modeling, or large-scale ranking systems
• Strong intuition for data quality and evaluation design in recommendation contexts (offline metrics, A/B testing, business metric alignment)
• Experience with large-scale data pipelines for user interaction data and feature engineering
• Proficiency in Python and PyTorch with autonomous coding and debugging ability

Tech Stack

• Python
• PyTorch

Benefits

• We pay 100% of medical, dental, and vision premiums for employees and dependents
• 401(k) matching up to 4% of base pay
• Unlimited PTO plus company-wide Refill Days throughout the year