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Matt has deep expertise in computer graphics and machine learning, and previously led the engineering team at Operator. We are highly technical and we are backed by AI and product experts from Google Brain, Salesforce Metamind and FAIR. Joe founded two successful companies prior to Facet: Metamarkets was acquired by Snap and Premise has expanded to provide basic income and data services to underserved communities in over 30 different countries.
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We are looking for a Data Engineer to build data pipelines and visualizations for key business metrics, machine learning (ML) models, and growth metrics to enable our data-driven organization.
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To do so we will use zebrafish as a model system and combine cutting-edge live microscopy, gene editing techniques, deep-learning-based data analysis, and mathematical modeling. We may also adjust this range in the future based on market data.
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Analytics: prior experience in data analytics or machine learning a strong plus. Evidence that elite defenders use to proactively hunt for threats, accelerate response to cyber incidents, gain complete network visibility and create powerful analytics using machine-learning and behavioral analysis tools.
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Leading the team through the life cycle of a D365 implementation providing key inputs during design, migrating data from external systems; conducting and supporting solution testing; building test cases; assisting with functional, system and user acceptance testing and conducting and supporting user and administrator training.
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Machine Learning Engineer San Francisco. You will:Design, build, and maintain large-scale services, data pipelines, tooling and systems for the Finance team led by Stripe’s CFO. Make direct business impact in some of Stripe’s most mission critical business domains including Accounting, Finance & Strategy, Treasury, and Business Operations.
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Our platform is an essential piece of the daily work for machine learning engineers, from academic research institutions like FAIR and UC Berkeley to massive enterprise teams including iRobot, OpenAI, Toyota Research Institute, Samsung, NVIDIA, Salesforce, Blue Cross Blue Shield, Lyft, and more.
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Proven experience with enterprise-scale technical delivery experience with AI Services including OpenAI, Schematic Kernel, AWS Bedrock, Machine Learning (or equivalent), Generative AI, LLM customization, NLP, Search, MLOps, Open-source AI frameworks, AI Infrastructure, architecture designAbility to align AI/ML initiatives with broader business goals and outcomes, understanding how AI can drive value and competitive advantage.
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Rosetta Stone is the leader in language learning, and our Enterprise product helps 12,000+ brands (such as BMW, Sony, and Williams-Sonoma) enable their employees to speak and work confidently across language barriers.
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Experience in learning design (e.g. Bloom taxonomy) and measurement (e.g. Kirkpatrick framework), enablement tooling (Learning management, content management, and Articulate/RISE), gamification, and program management across multiple stakeholders (i.e. "master herder of cats.
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Experience with Machine Learning and Data Science platforms like Databricks / Dataiku and experience with advanced data filtering systems. In this role you will work closely with our data science, machine learning, and data infrastructure team area of Commercial, Medical, and Government affairs.
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Proven in-depth experience in designing, developing, and implementing Data Management, Data Governance, Data Process, Data Quality, and Data Migration/ETL solutions.
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Proficiency in machine learning frameworks and libraries such as TensorFlow, PyTorch, or similar. Model Development: Develop, train, and fine-tune generative AI models using machine learning frameworks and techniques.
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Our lab is focused on leveraging cutting-edge experimental genomics and machine learning to map the gene networks driving cardiovascular disease and develop network-correcting therapies.
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Develop and maintain creative solutions to evolving challenges that span data processing, data modeling, and machine learning model deployment. Grow and maintain pipelines that power analytics, machine learning, and data products in our application layer.
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