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Strong programming skills in Python and proficiency with machine learning libraries such as TensorFlow, PyTorch, or JAX. Strong programming skills in Python and proficiency with machine learning libraries such as TensorFlow, PyTorch, or JAX.
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7-8 Years of strong expertise in R or Python programming languages and their application to data wrangling, machine learning (e.g., TensorFlow, PyTorch), and data visualization. Familiarity with modeling biological, cellular, or ecological data; molecular biology or biochemistry concepts; or data science in agriculture.
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Python, Shell Scripting and Linux-based operating systems with applications in machine learning and image processing. 3+ Years of experience in using TensorFlow and Tensor-RT. 5+ Years of experience in developing in Python for Machine Learning and Computer Vision applications.
$110,000 - $160,000 a yearFull-timeRemoteExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Proficiency in using ML frameworks (TensorFlow, PyTorch, sci-kit-learn) and languages (Python, R) for model development and experimentation. Proficiency in using ML frameworks (TensorFlow, PyTorch, sci-kit-learn) and languages (Python, R) for model development and experimentation.
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Experience deploying machine learning models in production (e.g., CI/CD pipeline development; containerization using tools such as docker, podman, or Kubernetes; Git) Experience and fundamental understanding of machine learning techniques (e.g., logistic regression, random forest, XGBoost, SVMs, K-means, neural networks.
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Proficiency in Google Cloud Platform (GCP) services relevant to machine learning and AI, such as AI Platform, BigQuery, Dataflow, and Tensorflow. Expertise in open source data science technologies such as Python, R, Spark, SQL.
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Proficient Coding Skills: Expertise in Python, R, or similar programming languages, and familiarity with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch. or JavaScript, and familiarity with AI libraries and frameworks like TensorFlow, PyTorch, Keras, and Scikit-learn.
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Strong expertise in R or Python programming languages and their application to data wrangling, machine learning (e.g., TensorFlow, PyTorch), and data visualization. M.S. or above in Applied Statistics, Artificial Intelligence, Biostatistics, Computer Science, Data Engineering, Data Science, Engineering, Machine Learning, Physics, Software Engineering, or related highly quantitative fields.
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Must have strong experience in R or Python programming languages and their application to data wrangling, machine learning (e.g., TensorFlow, PyTorch), and data visualization, Must have strong experience in R or Python programming languages and their application to data wrangling, machine learning (e.g., TensorFlow, PyTorch), and data visualization.
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Experience with machine learning frameworks such as TensorFlow or PyTorch. Strong programming skills in languages such as Python, R, or Java. We help organizations like Uber, GoDaddy, MGM, Siemens, and Stanford University build distributed software development teams, and deliver transformational digital solutions.
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Proven experience as a machine learning engineering or similar role with a strong focus on machine learning deployment and data pipeline construction, Stay abreast of emerging technologies in big data, machine learning, and agriculture tech and advocate for their adoption where beneficial.
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Experience in big data technologies (e.g., Hadoop, Spark) and database management systems (e.g., SQL, NoSQL) Responsibilities: Partner with R&D scientists to develop and prototype rigorous machine learning solutions aligned to project needs.
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Familiarity with artificial intelligence or generative AI techniques. The candidate must have experience and fundamental knowledge in machine learning, experience in deploying models, and programming skills to develop and deliver novel solutions in an industry setting.
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Expertise in programming languages such as Python, R, and SQL, with experience in advanced libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Expertise in programming languages such as Python, R, and SQL, with experience in advanced libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn.
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Experience with data manipulation libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., TensorFlow, Scikit-learn). Proficiency in programming languages like Python, R, or SQL for data analysis and modeling.
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