Lead Machine Learning Engineer, Recommendation Systems
Há 24 horas
Lead Machine Learning Engineer, Recommendation Systems at Launch Potato. We are hiring to build the personalization engine behind our portfolio of brands. You'll design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys, delivering 100M+ predictions daily and directly impacting engagement, retention, and revenue at scale.
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 experiences for millions of users daily. You'll own the modeling, feature engineering, data pipelines, and experimentation that 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 < 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
- Technical Mastery: You know ML architecture, deployment, and tradeoffs inside out
- Experimentation Infrastructure: You set up systems for rapid testing and retraining (MLflow, W&B)
- Impact-Driven: You design models that move revenue, retention, or engagement
- Collaborative: You thrive working with engineers, PMs, and analysts to scope features
- Analytical Thinking: You break down data trends and design rigorous test methodologies
- Ownership Mentality: You own your models post-deployment and continuously improve them
- Execution-Oriented: You deliver production-grade systems quickly without sacrificing rigor
- Curious & Innovative: You stay on top of ML advances and apply them to personalization
- Seniority level: Mid-Senior level
- Employment type: Full-time
- Job function: Engineering and Information Technology
- Industries: Advertising Services
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, status as a protected veteran, status as an individual with a disability, or other legally protected characteristics.
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