Senior ML Engineer, Recommendation Systems – Launch Potato
Join us as we build the personalization engine behind our portfolio of brands, delivering real‑time recommendations that influence engagement, retention, and revenue for millions of users daily.
About Launch Potato
Launch Potato is a profitable digital media company that reaches over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState.
As the Discovery and Conversion Company, our mission is to connect consumers with the world’s leading brands through data‑driven content and technology.
We are headquartered in South Florida with a remote‑first team spanning more than 15 countries, and we thrive on speed, ownership, and measurable impact.
Why Join Us
At Launch Potato, you’ll accelerate your career by owning outcomes, moving fast, and driving impact with a global team of high performers.
We convert audience attention into action through machine learning, continuous optimization, and engineering excellence.
MUST HAVE
- 5+ 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 Role
Your mission: Drive business growth by building and optimizing 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 for real‑time personalization with latency <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
Competencies
- Technical Mastery: Deep understanding of ML architecture, deployment, and trade‑offs
- Experimentation Infrastructure: Set up systems for rapid testing and retraining (MLflow, W&B)
- Impact-Driven: Design models that move revenue, retention, or engagement
- Collaborative: Thrive working with engineers, PMs, and analysts to scope features
- Analytical Thinking: Break down data trends and design rigorous test methodologies
- Ownership Mentality: Own models post‑deployment and continuously improve them
- Execution-Oriented: Deliver production‑grade systems quickly without sacrificing rigor
- Curiosity & Innovation: Stay on top of ML advances and apply them to personalization
Since day one, we’ve been committed to having a diverse, inclusive team and culture.
We are proud to be 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 applicable legally protected characteristics.
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