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.
38 Machine Learning Scientist jobs available for remote work from home. Apply for positions such as Machine Learning Scientist, Senior Machine Learning Scientist, Protein Design Scientist, Machine Learning 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.
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.
The Protein Design Scientist will leverage AI-first approaches and protein language models to design and optimize agricultural traits. They will collaborate cross-functionally with wet-lab teams to integrate experimental data into computational design loops.
You will lead the development and deployment of advanced machine learning models to enhance search, discovery, and personalization for over 15 million customers. You will act as a technical leader and individual contributor to build scalable, explainable AI solutions that solve complex financial problems.
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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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.
You will architect and execute machine learning solutions, including the development of LLMs and AI agents to automate complex data analytics workflows. Additionally, you will lead the AI stack roadmap, establish model governance standards, and mentor team members to drive business growth.
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.
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 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.
You will lead the development of advanced machine learning models and human-in-the-loop systems to optimize customer operations and query resolution. You will also provide technical leadership within the ML discipline, mentoring team members and steering technical strategy across product squads.
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 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.
Lead the design and deployment of advanced real-time ML models to detect financial crime and suspicious user behavior. Provide technical leadership and mentorship within the ML discipline while collaborating with cross-functional product squads.
Develop and deploy machine learning solutions to address high-impact societal challenges while collaborating with cross-functional teams and social sector organizations. Mentor junior researchers and contribute to academic research through publications in leading conferences and journals.
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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.
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.
Lead the development of deep learning models for TCR-pMHC specificity prediction by integrating sequence and structural information. Collaborate with cross-functional teams to translate biological principles into modeling decisions and drive clinical and commercial applications.
Own the end-to-end ML lifecycle to build ranking, retrieval, and LLM-powered agent systems for a fitness marketplace. Partner with product and engineering teams to ship models that drive user retention, booking lift, and GMV.
Lead the creation and optimization of advanced machine learning algorithms to enhance advertising effectiveness and ROI. Own the end-to-end development of production-grade ML models and collaborate with Data Engineers for system integration.
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.
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.
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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.
The Data Scientist will bridge the gap between theoretical model design and production reality by optimizing core Deep Neural Network models within a high-frequency auction ecosystem. Responsibilities include conducting deep-dive analysis of model behavior, formulating hypotheses, and designing experiments to improve bidding performance.
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.
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