Machine Learning Engineer, Data Science

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. 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™. HMH is a learning technology company committed to delivering connected solutions that engage learners, empower educators and improve student outcomes. The data covers millions of students, and the output lands in classrooms.
You would work across the full model lifecycle on AWS SageMaker, in Python: data, training, validation, release, serving, monitoring, and the next version. Engineering quality is the floor, whichever side you come from. We are hiring either a strong modeller who wants to run their models in production, or a strong ML engineer who wants to work on hard inference problems.
An ML engineer here works a model through its whole life, not one slice of a pipeline. Look hard at the data before you fit anything, and again after. A model earns its keep by surviving data it has not seen and by what it teaches you. SageMaker training jobs and pipelines in Python, experiments tracked in MLflow, validation against held-out cohorts before anything is promoted. Register, promote and deploy model versions with a lineage you can defend: which data, which code, which weights, served where. Serve.

Dashboards and alerts on prediction quality, failure rates and drift. Use what production tells you to propose the next version, and bring the analysis and evidence that shape the teams design docs (RFCs).
Help analysts, product and data teams understand what the models say and how to use them well.
These are the shapes of work on the team today, so you can judge whether the job is what you want.

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.
At home with ML and statistical models that can be explained: Writes clean, typed, tested Python and is comfortable with CI/CD and infrastructure as code. We will teach SageMaker and our platform.
Builds ML systems that are fast and resilient. Fluent in production Python, testing, packaging, GitHub Actions and Terraform. A track record of shipping models or ML systems that others depended on. that typically means three or more years of hands-on work, or a doctorate combined with relevant practical experience. A Masters or PhD in a quantitative field, psychometrics included, is expected.
Engineering excellence as the floor, not a bonus: idiomatic, typed, tested Python that passes review the first time; solid SQL. Python is our language.
A statistics foundation you can use, not just cite: you can explain a posterior, a likelihood and a calibration plot to a colleague without notes.
Exploratory data analysis as a discipline, not a warm-up. You look at the data before you model it, you notice what is odd, and you can show why it matters.
A model is only worth building if its reasoning can be read. 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.
It helps if you have:

psychometrics or education measurement; cognitive diagnostic models or Bayesian networks; experience with K-12 assessment data.
Nobody ships on a hunch, and nobody is blamed for a hunch that failed its test.
Analysis write-ups for every model, RFCs for design decisions, runbooks for operations, postmortems for incidents, data contracts between producers and consumers.
Model code is production code.

Typed Python, tests, review and CI on every change, for training pipelines as much as for endpoints. What we use:

AWS SageMaker (training jobs, pipelines, real-time endpoints, Model Registry, Feature Store), MLflow, Snowflake and dbt, Python, GitHub Actions, Terraform, Datadog, and knowledge graphs.
Location:

remote (may be performed from any state in the US)
We have wide ranges so we can be flexible and determine compensation based on a number of factors including the candidate’s skills, experience, education, and location.
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. HMH is fully committed to Equal Employment Opportunity and to attracting, retaining, developing and promoting the most qualified employees without regard to race, gender, color, religion, sexual orientation, family status, marital status, pregnancy, gender identity, ethnic/national origin, ancestry, age, disability, military status, genetic predisposition, citizenship status, status as a disabled veteran, recently separated veteran, Armed Forces service medal veteran, other covered veteran, or any other characteristic protected by federal, state or local law.