Lead Machine Learning Engineer, Recommendation Systems

Position Overview

We convert audience attention into action through data, machine learning, and continuous optimization.
We're hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands.
In this role, you’ll design, deploy, and scale ML systems that power real-time recommendations, serving 100M+ predictions daily and directly impacting engagement, retention, and revenue at scale.

Key Responsibilities

• Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale.
• Enhance data processing pipelines (e.g., Spark, Beam, Dask) with efficiency and reliability improvements.
• Design ranking algorithms that balance relevance, diversity, and revenue.
• Deliver real-time personalization with latency under 50ms across key product surfaces.
• Run statistically rigorous A/B tests to measure and optimize business impact.
• Optimize for latency, throughput, and cost efficiency in production environments.
• Collaborate with product, engineering, and analytics to launch high-impact personalization features.
• Implement monitoring systems and maintain clear ownership for model reliability.

Required Qualifications

• 7+ years building and scaling production ML systems with measurable business impact.
• Experience deploying ML systems serving 100M+ predictions daily.
• Strong background in ranking algorithms (collaborative filtering, learning-to-rank, deep learning).
• Proficiency with Python and ML frameworks such as TensorFlow or PyTorch.
• Skilled with SQL and modern data warehouses (Snowflake, BigQuery, Redshift) plus data lakes.
• Familiarity with distributed computing frameworks (Spark, Ray) and LLM/AI Agent frameworks.
• Track record of improving business KPIs via ML-powered personalization.
• Experience with A/B testing platforms and experiment logging best practices.

Preferred Qualifications

• Demonstrated technical mastery in ML architecture, deployment, and tradeoffs.
• Experience setting up rapid experimentation infrastructure (e.g., MLflow, W&B).
• A collaborative mindset paired with strong analytical thinking and ownership mentality.

Benefits & Perks

• Base Salary: $130,000-$250,000 per year, paid semi-monthly.
• Profit-sharing bonus included.
• Competitive Benefits and performance-based compensation increases.
• Compensation adjustments based on company and personal performance.