Top Remote Machine Learning Jobs
As a Security Governance Partner for Cash App's Machine Learning and Data Science, you will act as a security domain expert, collaborate with various teams, automate governance tasks, drive security requirements, and promote security standards and frameworks.
Seeking a Deep Learning Research Scientist to create novel deep learning systems and push the boundaries of AI capabilities. Fully remote position reporting to the Dessa AI Research Lead. Responsibilities include designing, developing generative AI systems, improving existing deep learning systems, and contributing to open-source projects.
Design, build, and manage distributed services and pipelines for Underwriting & Credit at Cash App. Lead projects, maintain code quality, collaborate with cross-functional teams, and contribute to technical problem-solving at scale.
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As a Senior Machine Learning Engineer at Wasabi, you will deploy scalable machine learning solutions and enhance computer vision and audio processing capabilities. You will work with a modern software stack and collaborate with a team of passionate engineers to drive innovation in machine learning and cloud storage.
Seeking a Machine Learning Architect to advance Machine Learning and Artificial Intelligence R&D strategy. Responsibilities include designing and developing AI architectures, evaluating AI technologies, collaborating across teams, and leading AI system deployment in production settings.
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Join Grammarly as a Machine Learning Engineer on the Marketing Technology team. You'll be responsible for improving data-driven decision-making, enhancing customer experiences, and optimizing marketing campaigns through the use of machine learning solutions. Collaborate with various teams to personalize marketing efforts and promote best practices in research and implementation.
Grammarly is seeking a Machine Learning Engineer to join their Marketing Technology team. The role involves shaping marketing strategies, improving customer experiences, and optimizing marketing campaigns using data and advanced algorithms. Responsibilities include maximizing ROI for advertising campaigns and delivering targeted ads to the right audience.
Lead the development of AI and Machine Learning solutions to enhance user experience and workflow efficiency for enterprise services at ServiceNow. Collaborate with a team of scientists, developers, and product managers to deliver innovative solutions to enterprise customers worldwide.
Seeking an Engineering Leader in Machine Learning for Security AI to lead teams, architect solutions, and drive innovation. Responsibilities include team leadership, architecture design, model development, training, and solution development in the realm of Security AI.
As a Machine Learning Engineer at Liftoff, you will own ML models with direct business impact, adopt new technologies, mentor team members, monitor ML research, and influence team roadmaps. Requires 8+ years of ML experience, strong engineering skills, and a degree in Machine Learning, Math, Physics or similar. Experience with AdTech is a plus.
The Staff Machine Learning Engineer role at Cruise involves driving applied ML research and development for urban roads related to Perception and Object Tracking. Responsibilities include exploring new algorithms, optimizing on-road performance, guiding technology choices, and enabling team members. Required qualifications include a PhD/MS degree in computer vision or machine learning, experience in deep learning projects, programming skills in Python and/or C++, expertise in ML frameworks, strong communication skills, and the ability to design scalable architectures.
Join Altice USA's Data Science team as a Machine Learning Engineer to deliver AI applications for various business units. Work on end-to-end solutions leveraging AI and ML principles, consult with stakeholders, analyze data, manage projects, share insights, and stay updated with industry trends.
Lead the development and implementation of analytical capabilities and applications, creation of machine learning models and pipelines, development of personalization services, technology proof of concept projects, standardization of machine learning algorithm framework, collaboration with Data Scientists and Analysts, addressing complex technical challenges, analysis of data for insights, and mentoring junior engineers.
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