Lead ML Engineer, Recommendation Systems
Há 4 dias
Lead ML Engineer, Recommendation Systems at Launch Potato. You will design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys, impacting engagement, retention, and revenue at scale. The role focuses on building the personalization engine behind a portfolio of brands and optimizing the end-to-end recommendation pipeline.
Launch Potato is a profitable digital media company with a remote-first team spanning 15+ countries. We serve over 30M+ monthly visitors and operate brands such as FinanceBuzz, All About Cookies, and OnlyInYourState.
Must Have- 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
Your mission: Drive business growth by building and optimizing the recommendation systems that personalize the experience for millions of users daily. You’ll own modeling, feature engineering, data pipelines, and experimentation to make personalization smarter, faster, and more impactful.
Outcomes- 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 under 50 ms 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
- Technical Mastery: ML architecture, deployment, and tradeoffs
- Experimentation Infrastructure: MLflow, Weights & Biases (W&B)
- Impact-Driven: Models that move revenue, retention, or engagement
- Collaborative: Works with engineers, PMs, and analysts to scope features
- Analytical Thinking: Designs rigorous test methodologies
- Ownership Mentality: Takes ownership of models post-deployment
- Execution-Oriented: Delivers production-grade systems quickly with rigor
- Curious & Innovative: Stays updated on ML advances and applies them to personalization
Equal Employment Opportunity: We are an Equal Employment Opportunity company. We value diversity, equity, and inclusion. We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, protected veteran status, disability, or other legally protected characteristics.
Job Details- Seniority level: Mid-Senior level
- Employment type: Full-time
- Job function: Engineering and Information Technology
- Industries: Advertising Services
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