AI/ML Applied Researcher - Augury

Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models. Research, design, and build anomaly classification, predictive, insights, recommendations and other types of models from various data sources, including sensor time-series data, textual data, images, and more. You will Ensemble multi-modalities by exploiting and enriching the most from our data collections platform and feature extraction and store phases, using classic statistical methods, deep learning, Transformers, GenAI, Graphs, Foundations Modules (as TSFM, Graph Transformer, TGNN), and any available technology to extract recommendations for taking real-world, life-changing actions Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data. Work with cloud-based big data platforms for training, distributed processing & experiments tracking (e.g., Metaflow, Grafana, Outerbounds, Databricks) Partnering and contributing to modern technologies: LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis M.Sc., or Ph.D. in Electrical Engineering, Computer Science, Physics, Mathematics, or a related field. 4+ years of experience in ML applications, including research on time-series data, ML-based vectoring, and Embeddings (Transformers included) Proficiency in Python and Applied Science SDLC - Discovery, Research, development, fidelity deployment, monitoring, Training, and fine tuning Experience in anomaly detection, abnormal behavior, outliers, pushing recall, precision, and tail metrics KPIs boundaries Experience working with ML ops infrastructure such as Vertex AI, Sagemaker etc. The ability to translate research into scalable, production-ready solutions. Experience working on Agile teams with a passion for fast iterations, feedback, and continuous learning. Proven ability to collaborate with diverse, cross-functional groups, including product managers, infrastructure, and data engineering teams. Experience in feature engineering-based signal processing - big advantage. Stock options Paid parental leave Flex PTO

Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models. Research, design, and build anomaly classification, predictive, insights, recommendations and other types of models from various data sources, including sensor time-series data, textual data, images, and more. You will Ensemble multi-modalities by exploiting and enriching the most from our data collections platform and feature extraction and store phases, using classic statistical methods, deep learning, Transformers, GenAI, Graphs, Foundations Modules (as TSFM, Graph Transformer, TGNN), and any available technology to extract recommendations for taking real-world, life-changing actions Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data. Work with cloud-based big data platforms for training, distributed processing & experiments tracking (e.g., Metaflow, Grafana, Outerbounds, Databricks) Partnering and contributing to modern technologies: LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis M.Sc., or Ph.D. in Electrical Engineering, Computer Science, Physics, Mathematics, or a related field. 4+ years of experience in ML applications, including research on time-series data, ML-based vectoring, and Embeddings (Transformers included) Proficiency in Python and Applied Science SDLC - Discovery, Research, development, fidelity deployment, monitoring, Training, and fine tuning Experience in anomaly detection, abnormal behavior, outliers, pushing recall, precision, and tail metrics KPIs boundaries Experience working with ML ops infrastructure such as Vertex AI, Sagemaker etc. The ability to translate research into scalable, production-ready solutions. Experience working on Agile teams with a passion for fast iterations, feedback, and continuous learning. Proven ability to collaborate with diverse, cross-functional groups, including product managers, infrastructure, and data engineering teams. Experience in feature engineering-based signal processing - big advantage. Stock options Paid parental leave Flex PTO

Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models. Research, design, and build anomaly classification, predictive, insights, recommendations and other types of models from various data sources, including sensor time-series data, textual data, images, and more. You will Ensemble multi-modalities by exploiting and enriching the most from our data collections platform and feature extraction and store phases, using classic statistical methods, deep learning, Transformers, GenAI, Graphs, Foundations Modules (as TSFM, Graph Transformer, TGNN), and any available technology to extract recommendations for taking real-world, life-changing actions Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data. Work with cloud-based big data platforms for training, distributed processing & experiments tracking (e.g., Metaflow, Grafana, Outerbounds, Databricks) Partnering and contributing to modern technologies: LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis M.Sc., or Ph.D. in Electrical Engineering, Computer Science, Physics, Mathematics, or a related field. 4+ years of experience in ML applications, including research on time-series data, ML-based vectoring, and Embeddings (Transformers included) Proficiency in Python and Applied Science SDLC - Discovery, Research, development, fidelity deployment, monitoring, Training, and fine tuning Experience in anomaly detection, abnormal behavior, outliers, pushing recall, precision, and tail metrics KPIs boundaries Experience working with ML ops infrastructure such as Vertex AI, Sagemaker etc. The ability to translate research into scalable, production-ready solutions. Experience working on Agile teams with a passion for fast iterations, feedback, and continuous learning. Proven ability to collaborate with diverse, cross-functional groups, including product managers, infrastructure, and data engineering teams. Experience in feature engineering-based signal processing - big advantage. Stock options Paid parental leave Flex PTO