Machine Learning Scientist
Design, train, and evaluate autoregressive and non-autoregressive speech synthesis models. Drive research on multi-modal architectures and collaborate with linguists to optimize TTS frontend behavior.
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Design, train, and evaluate autoregressive and non-autoregressive speech synthesis models. Drive research on multi-modal architectures and collaborate with linguists to optimize TTS frontend behavior.
The role involves setting the technical direction for user understanding within the ads organization by leveraging multimodal signals and LLM techniques. You will partner with ranking and bidding teams to integrate these signals into production while shaping the company's data strategy.
You will build and prototype supervised machine learning models to predict campaign delivery outcomes, replacing existing simulation engines. You will also partner with ML engineers to deploy these models at scale while ensuring they are explainable to non-technical stakeholders.
You will develop and optimize underwriting and credit strategies across multiple financial products using machine learning and alternative data. Additionally, you will design experimentation frameworks and partner with cross-functional teams to ensure scalable, compliant, and sustainable credit decisions.
Lead the development and implementation of advanced machine learning and Generative AI models to address critical healthcare challenges. Collaborate with cross-functional teams to define AI strategy and integrate scalable solutions into clinical and operational systems.
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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.
Design and productionize causal machine learning systems to optimize marketplace decisions across various business verticals. Develop frameworks for counterfactual evaluation and uplift modeling to improve consumer lifecycle value, promotions, and search ranking.
Design and implement deep learning architectures for 3D volumetric medical imaging and develop survival models for time-to-event prediction. Optimize large-scale training pipelines on cloud GPU infrastructure and contribute to research publications.
Develop and deploy production-ready ML and AI systems to optimize pricing, underwriting, and claims processes. Build end-to-end agentic workflows and measurement frameworks to improve business decision-making across the insurance value chain.
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.
Lead the development of machine learning methods and analyses of high-dimensional longitudinal patient data to generate clinical insights. Collaborate with cross-functional teams to build and operationalize ML pipelines within a computational platform for drug discovery.
Lead and drive ambitious research initiatives in computer vision and multimodal understanding to advance state-of-the-art models. Partner with engineering and product teams to translate research into practical, production-ready systems that enhance user experiences.
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.
You will own the end-to-end machine learning lifecycle, including model development, experimentation, and production deployment to detect fraud. You will also collaborate with cross-functional teams to translate complex fraud patterns into scalable, production-grade ML solutions.
Drive the end-to-end development and deployment of machine learning capabilities for customer-facing SaaS products. Partner with product and engineering teams to integrate ML models into workflows and establish evaluation strategies to measure business impact.
Own the end-to-end ML/AI lifecycle from defining success metrics and prototyping to deploying production-ready models. Collaborate with engineering and product teams to automate complex tax and accounting workflows using LLMs and traditional ML.
Design and develop geometry processing and simulation algorithms for engineering applications. Build services for processing 2D/3D engineering data and implement post-training workflows for machine learning models.
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