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 development. This role offers an exciting opportunity to impact millions of user experiences daily through sophisticated ML systems. As part of a remote-first team spanning 15+ countries, you'll be responsible for building and optimizing personalization engines that serve 100M+ predictions daily.
The ideal candidate will bring deep expertise in ranking algorithms, production ML systems, and data engineering. You'll work with cutting-edge technologies including distributed computing platforms, modern data warehouses, and ML frameworks to drive measurable business outcomes. The role combines technical leadership with hands-on development, requiring both architectural vision and practical implementation skills.
What makes this role unique is the scale and impact - you'll be directly influencing user engagement and revenue across multiple major digital brands reaching 30M+ monthly visitors. The position offers the chance to work with a high-performance team in a fast-paced environment where speed and ownership are valued. You'll have the autonomy to drive technical decisions while collaborating with cross-functional teams to deliver impactful personalization features.
The company culture emphasizes measurable impact, continuous optimization, and data-driven decision making. As a remote-first organization, they've built an inclusive environment that supports work-life balance while maintaining high performance standards. This role is perfect for an experienced ML engineer who wants to own and scale recommendation systems with direct business impact.
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
