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The Machine Learning Architect will guide teams through Agile ceremonies and translate customer requirements into actionable tasks for engineers. They will also lead discussions on architecture and troubleshoot issues in various environments.
The Machine Learning Architect will guide teams through Agile ceremonies and translate customer requirements into actionable tasks for engineers. They will also lead discussions on architecture and ensure the delivery of high-quality cloud native applications on AWS.
The Senior Machine Learning Engineer will work as part of a cross-functional delivery team to design and document machine learning solutions on the AWS cloud for customers. They will develop solutions designed by an architect and participate in daily standup meetings and bi-weekly agile ceremonies.
You will guide Cayliens and customers through Agile ceremonies and translate customer requirements into a workable backlog for engineers. Additionally, you will lead discussions on requirements gathering and architecture while troubleshooting issues in various environments.
Drive the design and development of large-scale ML/optimization systems for personalization efforts across the DashPass Subscriber journey. Partner closely with Product, Data Science, and Engineering teams to design experiments and production ML systems that impact subscriber growth metrics.
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The Machine Learning Engineer will work closely with the Data Science team to develop and implement ML algorithms into production code. They will ensure that algorithms are optimized for performance and scalability while collaborating with cross-functional teams.
You will help design, implement, test, and refine novel elements of a machine learning architecture built from the ground up to optimize the properties of small molecule drugs. You will also continually improve the robustness of our existing code base and apply our pipeline to new drug targets.
The role involves conducting research in machine learning and computer vision to develop algorithms for spatial understanding and digital twin creation. Additionally, the engineer will collaborate with teams to translate research into production-ready solutions.
You will build, train, and deploy ML models to enhance product performance and monetization. The role involves collaborating with various teams and working on projects related to LLMs and Generative AI technologies.
Train, fine-tune, and deploy generative models across multiple modalities while optimizing model inference performance. Collaborate closely with product and engineering teams to implement AI features in the application.
Train state of the art models and research cutting edge algorithms for enterprise customers. Build agents that leverage proprietary algorithms to optimize datasets.
You will build synthetic data pipelines to generate enterprise environments for reinforcement learning post-training and create agents to convert production traces into actionable insights. Additionally, you will contribute to the agent building product and train state-of-the-art models for enterprise deployment.
Lead the development and scaling of the Wildfire Fuel Detection Model, focusing on understanding vegetation structure and wildfire risk. Collaborate with data scientists and product teams to ensure models are accurate and production-ready.
You will build and maintain the infrastructure that supports Netflix's large-scale machine learning and AI systems. This involves working closely with modeling teams on model-system codesign to optimize training, inference, and serving platforms.
Build infrastructure and software for training, deploying, and scaling machine learning models and optimization algorithms within supply chain applications. Translate real-world supply chain use cases into mathematical models and lead the design of ML systems.
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