10 Machine Learning Scientist jobs in Panama available for remote work from home. Apply for positions such as Machine Learning Scientist 4 - Generative Models, Evaluation, Machine Learning Scientist 6 - Ads Member Understanding, Machine Learning Scientist 5- Forecasting Aggregation and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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The scientist will design and develop innovative machine learning solutions, specifically focusing on generative models and LLM-based evaluators. They will lead end-to-end ML development and partner with cross-functional teams to integrate these models into business applications.
Lead the user understanding charter by leveraging multimodal signals and LLM techniques to build a foundation for ad targeting, ranking, and bidding. Partner with cross-functional teams to define data strategy and implement rigorous evaluation frameworks for production ML systems.
You will build and prototype supervised machine learning models to predict campaign delivery outcomes, replacing existing simulation engines. Additionally, you will partner with ML engineers to deploy these models at scale while ensuring they are interpretable for sales and media-planning stakeholders.
Design and develop innovative ML solutions to enhance promotional writing and content discovery for Netflix members. Lead end-to-end development from research and model training to integration into business platforms.
Define and drive the ML technical roadmap for Live Ads, focusing on forecasting, targeting, and yield optimization. Collaborate across teams to architect and deploy large-scale ML solutions that improve ad quality and performance.
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Build and iterate on supervised machine learning models to predict ad campaign delivery outcomes, replacing a simulation-based engine. Partner with ML engineers to deploy these models at scale and collaborate with cross-functional teams to drive adoption of ML-driven forecasts.
Lead the technical vision for AI/ML to automate sales workflows and deploy AI-augmented selling tools. Partner with product and engineering teams to build production models that increase sales productivity and revenue.
Design and implement ML-driven bidding algorithms to optimize ad performance metrics like Clicks, Conversions, and ROAS. Collaborate with product teams to align auction mechanisms and pricing with business goals and revenue objectives.
Design and implement machine learning and optimization algorithms to enhance ad quality, performance, and yield within the ad marketplace. Collaborate with product teams to define business objectives and communicate technical results to stakeholders.
Design and implement machine learning and optimization algorithms to enhance ad quality and performance. Collaborate with product teams to define optimization objectives and communicate technical results to stakeholders.
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