Lead Machine Learning Engineer, Ad Performance

Lead Machine Learning Engineer, Ad PerformanceAs The Discovery and Conversion Company, our mission is to connect consumers with the world's leading brands through data-driven content and technology.Headquartered in South Florida with a remote-first team spanning over 15 countries, we've built a high-growth, high-performance culture where speed, ownership, and measurable impact drive success.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 turn audience attention into action through data, machine learning, and relentless optimization.Are you a Machine Learning Engineer with a passion for bridging technical expertise and business acumen?Do you thrive in building scalable, reliable systems that directly move revenue?If you're eager to collaborate globally, face challenges head-on, and make substantial impact, we're looking for you to join our dynamic team.In this role, you'll work at the intersection of advertising algorithms, personalization, and optimization... building the ML software and infrastructure that drives ad performance and user engagement at scale.MUST HAVEYou bring both the technical depth to design and deploy complex ML systems and the business intuition to know what actually moves KPIs.Specifically:7+ years of software development and ML engineering, including 3+ years focused on advertising systemsProven experience optimizing ad performance using both mature models and emerging ML technologiesExpertise in one or more ML areas: Recommendation Systems, Advertising Algorithms, NLP, or CVProficiency with Python, SQL, and modern ML frameworks (TensorFlow, PyTorch)Familiarity with distributed data processing (Spark, Ray) and cloud infrastructure (AWS/GCP)Strong analytical problem-solving skills with a proven track record of delivering measurable resultsExcellent communication, teamwork, and project management skillsYOUR ROLEYour mission: Elevate Launch Potato's advertising performance by building scalable, production-ready ML systems that optimize revenue while enhancing user experience.You'll collaborate across teams to design, deploy, and refine algorithms that improve ad targeting, relevance, and efficiency at global scale.OUTCOMESDevelop and scale advertising algorithms that optimize engagement and revenue simultaneouslyApply cutting-edge ML approaches (e.g., reinforcement learning, deep learning for ranking) to improve ad performanceBuild scalable infrastructure to support real-time ad decisioning across millions of requests per dayConduct rigorous experiments (A/B and multivariate tests) to measure model impact on business KPIsContinuously improve ETL pipelines and monitoring systems to ensure reliable ad deliveryCollaborate with global teams across product, data, and engineering to launch high-impact ad featuresStay ahead of emerging technologies and integrate them into our ML stack when they create competitive advantageCOMPETENCIESImpact-Oriented: You align ML systems to business outcomes and measure what mattersTechnical Builder: You write clean, production-ready code and architect scalable systemsCollaborative: You work seamlessly with global teams and communicate complex ideas clearlyResilient & Determined: You don't shy from challenges and drive projects through to impactCurious Innovator: You keep pace with ML/AI trends and creatively apply them to advertisingAnalytical Problem-Solver: You break down complex systems and find data-driven solutionsWe are 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.#J-*****-Ljbffr