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 developing novel Machine Learning and Deep Learning methods for medical image analysis, including segmentation, detection, and outcome prediction, while ensuring these methods are scientifically rigorous and clinically meaningful. Responsibilities also include publishing findings in top-tier venues and collaborating with clinicians to translate research into validated outputs for production engineering and regulatory submissions.
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.
Develop and deploy multimodal deep learning models integrating imaging, genomic, and clinical data to advance oncology diagnostics. Collaborate with cross-functional teams to scale machine learning workflows and translate prototypes into validated, production-quality tools.
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.
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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.
You will develop and deploy predictive machine learning models to identify fraud, waste, and abuse within a large-scale healthcare claims warehouse. This role involves establishing modeling practices, collaborating with subject matter experts, and ensuring model output provides actionable evidence for investigators.
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.
Lead the research and development of health trajectory models and next-best-action algorithms to improve member outcomes. Provide technical leadership and mentorship to scientists while collaborating with product and engineering teams to productionize ML models.
Lead the architecture and validation of genomic and multimodal foundation models to accelerate patient stratification and therapeutic monitoring. Bridge the gap between AI research and clinical science to transition representation learning models into reproducible diagnostic assets.
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.
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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.
The Research Scientist will apply machine learning to Navy numerical weather prediction development and testing. Responsibilities include evaluating high-resolution AI weather systems, transitioning global ML models into operations, and investigating multiscale predictability of atmospheric phenomena.
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