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Must have 3 yrs of exp w/: machine learning, math modeling, & stats; implementing productn machine learning systems at scale in Python; data modeling; TensorFlow/Keras & Scikit-Learn; SQL; Big Data platfrms such as BigQuery, Redshift, Hive/Hadoop, or Snowflake; & cloud platfoms such as GCP, AWS, or Azure.
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A Glimpse into the Daily Routine of a Machine Learning Engineer. Self-motivated and proactive, with a passion for learning and staying updated with the latest trends and advancements in the field of data engineering.
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Reporting to the Director of Machine Learning Engineering the role requires a skilled Machine Learning Engineer to design, build, and deploy machine learning models that will help us derive actionable insights and solve complex problems.
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A graduate degree (Masters/PhD) in computer science, computer engineering, electrical engineering, mechanical engineering, aerospace engineering, or similar technical discipline with an emphasis on artificial intelligence and machine learning.
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As part of the Data Analytics team, the Lead Data Scientist will work closely with the Data Engineering team and business functions to solve real-world oil and gas midstream problems using machine learning, data science algorithms and artificial intelligence.
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The candidate will have the opportunity work on a wide range of use cases from simple customer facing data APIs to large scale machine learning models. Strong analytical background with an expert knowledge of ML and Deep Learning packages.
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Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics.
$150Full-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience with applying machine learning to real-world problems leveraging NLP, LLM Agents, LLM Fine-tuning, Recommendation Systems, Deep learning etc. As a Staff Machine Learning Engineer/Tech Lead, you will be responsible for leading the development and deployment of machine learning models and systems for our organization.
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Our Client, a forward-thinking start-up at the forefront of quantum computing and artificial intelligence, is seeking a dynamic individual to join their team as a Machine Learning Engineer.
$225,000 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Implement highly scalable platform components and tools using machine learning and deep learning models to solve real-world problems in areas such as Speech Recognition, Natural Language Processing, Best Next Action, and Time Series predictions.
$110,000 - $190,000 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Avery Dennison is seeking a Materials Scientist to meet organizational needs for accelerated weathering testing, outdoor aging, analytical testing, data analysis/machine learning, and specific weathering research projects with pressure sensitive adhesive film constructions for Graphics & Durables applications.
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Work closely with cross-functional teams, including computational chemistry, bioinformatics, and medicinal chemistry, to integrate machine learning solutions. This is a strategic leadership role that requires a deep understanding of machine learning, drug discovery, and computational chemistry.
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As a Senior ML Ops Engineer at SiriusXM, you will be a key player in our Data Platform Team. Your role will be pivotal in deploying, managing, and optimizing machine learning (ML) models, leveraging advanced tools like Databricks and MLFlow.
$135,000 - $209,500 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Collaborate with data science and data engineering team to build scalable and reproducible machine learning pipelines for training and inference. Solid understanding of machine learning life cycle, data mining, and ETL techniques.
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Skills Required: Requires experience in the following: Natural Language Processing (NLP) in a commercial environment; programming expertise in Python; Agile development paradigm; machine learning APIs and computational packages including Scikit-Learn, NumPy, SciPy, and Pandas; neural network packages such as PyTorch or TensorFlow; and development in Amazon Web Services (AWS) environments.
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