Machine Learning Engineer

NWEA is a division of Houghton Mifflin Harcourt that supports students and educators through research, assessment solutions, policy and advocacy services, professional learning and school improvement services that fight for equity, drive classroom impact and push for systemic change in our educational communities. For nearly 50 years, NWEA has developed innovative pre-K–12 assessments, including their flagship interim assessment, MAP Growth and their reading fluency and comprehension assessment, MAP Reading Fluency. For more information, visit NWEA.org to learn more.

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.

• 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.

HMH is fully committed to Equal Employment Opportunity and to attracting, retaining, developing and promoting the most qualified employees without discrimination because of race, religion, gender, civil status, family status, sexual orientation, disability, age, membership of the Traveller community or any other protected characteristic which may be established by law. We are dedicated to providing a work environment free from discrimination and harassment, and where employees are treated with respect and dignity.