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This candidate should have experience in at least one area of deep learning but have familiarity with a broad array of techniques, such as data pipeline development, metabolic engineering, or protein engineering.
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The BCG Global Learning & Development Design Center has established an Innovation Center of Excellence (CoE) to enhance and modernize the learning experience for BCG employees globally and future-proof our learning function.
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Lead the development and implementation of a comprehensive K-12 Tier 1 social emotional learning plan for the district. Lead implementation of school systems, environment and instructional practice strategies that promote positive social emotional learning and development.
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The position aims for candidates with a strong quantitative and computational background with extensive experience in genomics/transcriptomics (analysis of different bulk/single cell/spatial Next Generation sequencing Techniques as well as development of novel methods.
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Should have a strong background in at least two of the following areas: Algorithm Development, Machine Learning/AI and Data Analysis, Mathematical Modeling and Simulation, Digital Signal Processing, Control Algorithms Design.
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As an ML Engineer, you will lead the development and deployment of innovative approaches to optimize existing and new machine learning systems, unlocking their full potential to drive maximum business value.
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Expertise in scientific programming languages (e.g., Python, R) and machine learning libraries (e.g., PyTorch, Tensorflow) We seek an enthusiastic and collaborative machine learning scientist with a focus on AI/ML methods to join our Small Molecule Drug Discovery team.
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Technical familiarity: Knowledge of cell line development and upstream process development for biologics and/or viral vectors. You'll be able to grow into a business development role at a nimble, forward-thinking synthetic biology startup with a global reach.
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As a Software Development Manager for the AEC Solutions group, you will lead your team to build data pipelines to power our work in artificial intelligence, deep learning, generative AI, machine learning, reinforcement learning, information retrieval, and natural language processing.
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Key medicinal chemistry principles to your cross-disciplinary colleagues from Computational Chemistry, AI Research, Machine Learning Engineering, Cell Biology, and Pharmacology. Effective collaboration with the ML Engineering and AI Research team by providing key medicinal chemistry insights to aid in the development of AI/ML models for drug discovery.
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Iris is Pluralsight’s learning intelligence platform, an innovative and unique user experience, whose aim is to use data to create a smarter, personalized learning journey. We are growing a team of people from multiple disciplines who love solving complex problems with data and are excited by the prospect of creating the brains behind Iris. As a Machine Learning Engineer, you will be responsible for building the infrastructure and implementing the algorithms that make Iris smart.
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Strong understanding of fundamental computer science concepts, software design best practices, software development lifecycle and common machine learning design patterns. 10+ years of software engineering, analytics development or machine learning engineering experience.
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Development and implementation of advanced data analytics and state-of-the-art machine learning models to generate insights from proteomics (ABPP) data, protein structure, and protein dynamics to enhance target prioritization and structure-based drug design efforts.
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This includes being an asset to the Customer Success Manager, Learning & Development Manager, and Integrations and/or Solutions Engineer while being an advocate for the customer. This includes being an asset to the Customer Success Manager, Learning & Development Manager, and Integrations and/or Solutions Engineer while being an advocate for the customer.
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Strong background and successful track record in bioanalytical assay development, optimization, and troubleshooting with experience in: transcriptomics, pharmacogenomic analyses and interpretation, biomarkers (including pharmacodynamic biomarkers), flow cytometry, PK, ADA, and nAb methods.
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