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.
31 Machine Learning Scientist jobs in United States available for remote work from home. Apply for positions such as Machine Learning Scientist, Machine Learning Scientist, Machine Learning Scientist 6 - Ads Member Understanding and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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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 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.
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.
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You will be responsible for developing, deploying, and maintaining production-grade machine learning models within a cloud environment. The role involves collaborating with cross-functional teams to build scalable data pipelines and enhance product offerings using AI and LLM capabilities.
The Staff Machine Learning Scientist will lead the development and technical roadmap for personalization and recommender systems across digital touchpoints. This role involves managing end-to-end model development, mentoring team members, and collaborating with stakeholders to drive business outcomes.
The role involves owning end-to-end machine learning systems for forecasting, including development, deployment, and monitoring. You will also build AI-assisted development workflows and partner with cross-functional teams to translate business needs into technical roadmaps.
The role involves owning end-to-end machine learning systems for forecasting, including model development, deployment, and monitoring. You will also build AI-assisted development workflows and partner with cross-functional teams to translate business needs into technical roadmaps.
The role involves owning end-to-end machine learning systems for forecasting, including model development, deployment, and monitoring. You will also build AI-assisted development workflows and partner with cross-functional teams to translate business needs into technical roadmaps.
You will be responsible for developing, deploying, and maintaining production machine learning models within a cloud environment. Additionally, you will collaborate with cross-functional teams to build scalable data pipelines and support data-driven decision-making.
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.
Design, build, and operate scalable machine learning and AI systems to power business-critical decision making and clinical operations. Own the end-to-end ML lifecycle from data engineering and model training to deployment and production monitoring.
Lead the end-to-end development and deployment of personalization products and recommender systems to improve book discovery. Define the technical roadmap, mentor other scientists, and collaborate with stakeholders to align ML strategy with business goals.
Lead the end-to-end development and deployment of personalization products and recommender systems to improve book discovery. Define the technical roadmap, mentor other scientists, and collaborate with stakeholders to align AI strategy with business goals.
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Build, validate, and improve machine learning models to help cities prioritize infrastructure investments and reduce risk. Design new model products and optimize data science workflows for integration into SaaS product systems.
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.
Develop novel machine learning algorithms for applications in energy, healthcare, and robotics. Create software simulations and prototype systems to analyze and prove the performance and practicality of these algorithms.
Design and build agentic systems to automate the acquisition, cleaning, and quality control of large-scale biomedical datasets for the Enchant model. Collaborate with ML scientists to translate data requirements into scalable processing pipelines and maintain production robustness.
Research, develop, and scale the Enchant multimodal transformer model to advance AI-based drug discovery. This includes optimizing training pipelines, designing evaluation frameworks, and collaborating with cross-functional scientists to deploy models into production.
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.
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The Principal Machine Learning Scientist will advance core computer vision model performance for warehouse inventory scanning across drone and MHE Vision platforms, owning the full ML lifecycle from research through production deployment and monitoring. This includes collaborating on model optimization for various inference targets and providing technical leadership and mentorship to the ML team.
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.
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