Lead Machine Learning Engineer, Recommendation Systems
Job Description
Launch Potato, a thriving digital media company, is seeking a Lead Machine Learning Engineer to spearhead their recommendation systems. This role offers an exciting opportunity to impact millions of user experiences through sophisticated ML systems serving 100M+ predictions daily. As part of a remote-first team spanning 15+ countries, you'll be responsible for building and optimizing personalization engines that drive engagement, retention, and revenue at scale.
The position requires deep expertise in ML architecture, ranking algorithms, and production deployment. You'll work with cutting-edge technologies including distributed computing systems, modern data warehouses, and LLM frameworks. The role combines technical leadership with hands-on implementation, requiring both strategic thinking and practical engineering skills.
Launch Potato offers a competitive compensation package including profit-sharing and comprehensive benefits. The company maintains a high-performance culture focused on measurable impact and rapid execution. You'll join a diverse, global team that values speed, ownership, and data-driven decision making.
This is an ideal opportunity for an experienced ML engineer who wants to work on large-scale personalization systems while having direct impact on business outcomes. The role offers significant autonomy and the chance to shape the technical direction of recommendation systems across multiple successful digital brands.
Responsibilities For Lead Machine Learning Engineer, Recommendation Systems
- Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale
- Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements
- Design ranking algorithms that balance relevance, diversity, and revenue
- Deliver real-time personalization with latency <50ms across key product surfaces
- Run statistically rigorous A/B tests to measure true business impact
- Optimize for latency, throughput, and cost efficiency in production
- Partner with product, engineering, and analytics to launch high-impact personalization features
- Implement monitoring systems and maintain clear ownership for model reliability
Requirements For Lead Machine Learning Engineer, Recommendation Systems
- 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 (TensorFlow or PyTorch)
- Skilled with SQL and modern data warehouses (Snowflake, BigQuery, Redshift) plus data lakes
- Familiarity with distributed computing (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
