Machine Learning Engineer

HMH is hiring a Machine Learning Engineer to build and operate explainable models for HMH Pulse using student assessment data. The role spans the full model lifecycle, including data analysis, training, validation, deployment, serving, monitoring, and improvement, while supporting teams that use the models.

Responsibilities:

• Explore and check. 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. You will write that up for people who will challenge it
• Train and validate. SageMaker training jobs and pipelines in Python, experiments tracked in MLflow, validation against held-out cohorts before anything is promoted. You should be able to defend every parameter
• Release. Register, promote and deploy model versions with a lineage you can defend: which data, which code, which weights, served where. Releases go through code review and CI like any other change
• Serve. Real-time endpoints and batch scoring, fed from a feature store and backed by Snowflake. Latency, cost and capacity are yours to understand and tune
• Watch. Dashboards and alerts on prediction quality, failure rates and drift. A monthly production-health review. A blameless postmortem when something breaks, and the fix that follows
• Improve. Use what production tells you to propose the next version, and bring the analysis and evidence that shape the team's design docs (RFCs)
• Support. Help analysts, product and data teams understand what the models say and how to use them well