Machine Learning Engineer

March 7

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HMH

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HMH is an educational company that specializes in providing comprehensive curriculum and assessment solutions across a wide spectrum of subjects. They offer programs and resources for literacy, mathematics, science, social studies, and social-emotional learning for K-12 students. Additionally, HMH focuses on professional development for educators to enhance instructional strategies and improve student outcomes.

Education • Learning Technology

1001 - 5000 employees

📚 Education

🛍️ eCommerce

📋 Description

• The Machine Learning Engineer (ML engineer) contributes to the design, development, and deployment of real-world machine learning systems that facilitate and accelerate advanced AI solutions at scale. • Machine learning engineers manage aspects of the machine learning pipeline, including sourcing and preparing data, building, and training models, and deploying to and monitoring models in production. • Manage, maintain, scale, and improve machine learning models already running in production environments. • Design, test, and deploy models to production, initially as a prototype, and then as an API that can serve predictions for end users. • Design, run, and evaluate machine learning tests and experiments. • Optimize and tweak machine learning models according to how they behave in production. • Use CI/CD processes throughout the model development lifecycle to develop higher quality code and models without disruption to production. • Provide technical support to data and product teams, helping relevant parties use and understand machine learning systems and datasets.

🎯 Requirements

• Masters or Ph.D. in computer science, engineering, or quantitative field. • 2-3 years of hands-on experience in machine learning, data science, and analytics. • Proficient with tools for machine learning pipeline management and monitoring (e.g., SageMaker, MLflow, Kubeflow). • Proficient with machine learning algorithms, architectures, programming languages (e.g., Python, C++, R), and libraries (e.g., PyTorch, TensorFlow). • Proficient with SQL and analytical warehouses (e.g., Snowflake). • Familiarity with automation and deployment (e.g., Jenkins, GitHub actions, Concourse, etc.) and infrastructure as code (e.g., Terraform) technologies. • Employed distributed processing frameworks such as Spark and Dask and has interacted with cloud platforms and container technologies. • Practical knowledge of software engineering concepts and best practices, like testing frameworks, packaging, API design, DevOps, DataOps and MLOps. • Excellent problem-solving skills and easily adapt to new technologies, trends, and frameworks. • Demonstrated ability to elevate the machine learning discipline and foster a collaborative and high-performing environment.