Senior ML Researcher

3 weeks ago


Los Angeles, United States Acceler8 Talent Full time

Senior ML Researcher (Distributed/Heterogeneous Machine Learning)If you are passionate about driving innovation in machine learning and computational technology, this could be the opportunity for you. We are seeking a Senior Machine Learning Scientist to enhance our capabilities in high-performance computing for AI applications.Our organization is a leader in advanced machine learning compute solutions, focusing on developing scalable and efficient systems that power the next generation of AI technologies. We bridge the gap between theoretical AI and practical applications, ensuring that complex machine learning models are accessible and executable on a global scale.In the role of Senior Machine Learning Scientist, you will be pivotal in advancing our computational frameworks and developing methods that significantly reduce the time and resources required for complex ML model training. Your expertise will directly contribute to innovations that shape the future of machine learning technology.As part of this role you will be able to publish papers.What We Can Offer YouCompetitive salary with comprehensive benefits, including health, vision, and dental insuranceSupport for professional development, including conference attendance and continuous training opportunitiesState-of-the-art technology and tools to facilitate your work and researchRegular team retreats, providing opportunities for collaboration and growthKey ResponsibilitiesDevelop and optimize ML computational frameworks to enhance performance and scalabilityConduct rigorous research and experimentation to pioneer new methodologies in machine learning computeCollaborate with a team of experts to integrate cutting-edge research into practical applicationsDocument and disseminate findings, including publishing papers in high-impact journals and presentations at leading conferencesMentor junior scientists and engineers, guiding them in best practices and advanced computational techniquesDistributed computing, heterogeneous data sources, parallel processing, cluster computing, grid computing, scalability, multi-node architecture, data partitioning, load balancing, high-performance computing (HPC), fault tolerance, resource allocation, big data integration, asynchronous communication, GPU/CPU collaboration, cross-platform optimization, federated learning, edge computing, model sharding, decentralized data processing, cloud-based ML infrastructure, multi-agent systems, data streaming, inter-process communication, model replication and synchronization.#J-18808-Ljbffr

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