[Hiring] Machine Learning Engineer @HMH

Role Description

NWEA® is a division of HMH 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.

You would work across the full model lifecycle on AWS SageMaker, in Python:

• Data
• Training
• Validation
• Release
• Serving
• Monitoring
• The next version

An ML engineer here works a model through its whole life, not one slice of a pipeline. You will sit on a stream-aligned team, working with a senior or staff engineer, and you will be the person who knows your models best.

What you will do

• Explore and check: Look hard at the data before you fit anything, and again after.
• Train and validate: SageMaker training jobs and pipelines in Python, experiments tracked in MLflow.
• Release: Register, promote and deploy model versions with a lineage you can defend.
• Serve: Real-time endpoints and batch scoring, fed from a feature store and backed by Snowflake.
• Watch: Dashboards and alerts on prediction quality, failure rates and drift.
• Improve: Use what production tells you to propose the next version.
• Support: Help analysts, product and data teams understand what the models say and how to use them well.

The kind of problems you will work on

• Take a model that works for one subject or one curriculum and make it work for several.
• Re-score a very large student population against a new model version while the live service keeps serving.
• Run several model versions side by side and retire one without anyone noticing.
• Catch data and model drift before a user does.
• Make training and serving more efficient without changing what a model says.

Qualifications

• A track record of shipping models or ML systems that others depended on.
• A Masters or PhD in a quantitative field, psychometrics included, is expected.
• Engineering excellence as the floor: idiomatic, typed, tested Python.
• A statistics foundation you can use, not just cite.
• Exploratory data analysis as a discipline.
• Excellent writing skills.
• Working knowledge of AWS, ideally SageMaker, and of an experiment tracker such as MLflow.
• The confidence to say "this is not good enough to ship" and the evidence to back it up.

Requirements

• Strong on one of these two sides and curious about the other.
• The modeller: At home with ML and statistical models that can be explained.
• The engineer: Builds ML systems that are fast and resilient.
• Psychometricians: If you have built or evaluated measurement models, write good code already, and want to move into engineering.

Benefits

• Salary Range – $110,000 to $115,000.
• Flexible compensation based on skills, experience, education, and location.
• Culture and Benefits information available at this link .

Application Deadline

The application window for this position is anticipated to close on October 23, 2026. We encourage you to apply as soon as possible.