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This role requires strong experience in leading research and development in digital health, especially applying AI, Machine Learning, and Generative AI / LLM to develop novel health products that leverage wearables, smartphones, apps, and health data.
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What we need to see:BS (or equivalent experience) in Computer Science or Electrical or Computer Engineering and 5 years of CAD/software development experience; MS preferred with 3 years of CAD/software development experienceSoftware engineering experience with software design, algorithms, data structures, testingFamiliarity with Verilog and ASIC and physical design along with experience in commercial EDA toolsStrong proficiency in at least one of Python, C/C.
$128,000 - $258,750 a yearFull-timeExpandApply NowActive JobUpdated 22 days ago - UpvoteDownvoteShare Job
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PhD in Electrical Engineering, Computer Science, Robotics, or a related field, with a focus on sensor fusion, computer vision, signal processing, and/or machine learning. Work closely with the data engineering team to build and maintain large-scale datasets for training and evaluating perception models, including the development of data augmentation techniques.
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BSc above in Machine Learning, Computer Science, Engineering, or a related technical discipline or equivalent experience. MS, or above in Machine Learning, Computer Science, Engineering, or a related technical discipline or equivalent experience.
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Proficiency in ASIC development workflow including hands-on experience in one or more the following domains: Architecture, RTL/Analog design, Design Verification, Physical Design, Layout, post-silicon Validation/Characterization, Product or Test Engineering •Experience in solving technical problems in pre-silicon or post-silicon environment.
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Apache Spark is a multi-language engine for data engineering, data science, and machine learning that lies at the core of our initiatives. Lead a high-performing engineering team in the development of the Spark platform, reinforcing Apache Spark’s position as the world’s leading open-source unified engine for large-scale data analytics.
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Research, design and implement scalable machine learning (ML) models for recommender systems, and/or other scalable computational models in an iterative product cycle. With a problem solving attitude, map the product and business requirements to an machine learning/AI problem.
$117,000 - $234,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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You'll be partnering with product management, user experience, customer success, machine learning and other engineering teams to identify, define and build elegant solutions.
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Conduct research to conceptualize, design, and/or implement innovative solutions for autonomous driving including but not limited to applications of artificial intelligence, machine learning, optimization, and probabilistic robotics.
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Familiarity with popular machine learning frameworks, such as PyTorch and tensorflow. The company focuses on short-haul, B2B logistics for Fortune 500 customers including Kroger, Walmart, Tyson Foods, Loblaw, Pitney Bowes, Georgia-Pacific, and KBX; enabling them to optimize their hub-and-spoke supply chain operations, enhance service levels and product flow across multiple locations while reducing labor costs and meeting an unprecedented expectation for faster deliveries.
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Familiar with one machine learning development framework such as PyTorch, TensorFlow, MXNet. You will work with a team of machine learning researchers to build AI software systems, learn about deep learning algorithms, and use your technical skills to advance autonomous driving.
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MBRDNA is headquartered in Silicon Valley, California, with key areas of Autonomous Driving, Advanced Interaction Design, Digital User Experience, Machine Learning, Customer Research, and Open Innovation.
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B.S. with 2-3+ years of experience or M.S with 1+ in a Bioengineering, mechanical engineering, electrical engineering, computer engineering, mechatronics, or a related discipleTroubleshooting experience in development or operations of microfluidics, liquid handler, diagnostic systems, NGS systems.
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Accepted degrees in Electrical Engineering, Materials Science, Physics, Chemical Engineering or other relevant engineering or physical science discipline. PhD degree in Electrical Engineering, Materials Science, Chemical Engineering, Chemistry, Physics or related field.
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Experience with popular MLOps tooling from cloud vendors like GCP (Vertex AI), AWS (SageMaker), or Azure Machine Learning, MLFlow, Kubeflow, etc. Work on advanced tools developed for the world’s leading cloud-native machine learning engine that uses graph deep learning technology.
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