Senior ML Engineer — IoT Analytics for Water Tech

Badger Meter Inc; Milwaukee (WI); Hybrid; USD 120,000 - 170,000; Full time

Benefits offered by this job

Competitive pay

Annual bonus

Health, vision, dental coverage

15 days PTO + 11 holidays

Retirement plan with company match

Job summary

Badger Meter is seeking a Senior Engineer, Machine Learning in Milwaukee, WI to lead ML initiatives across large-scale IoT data from meters, sensors, and connectivity devices. You will define scope, build end-to-end pipelines, and monitor model performance in production, working with data engineers and software teams to deliver reliable, scalable ML solutions.

The role emphasizes leadership, collaboration with cross-functional teams, and the development of predictive models and anomaly detection

Qualifications

• 5+ years of related experience in machine learning, data science, or applied analytics.
• Strong foundation in engineering and statistical fundamentals.
• Experience with cloud-based data and ML platforms, preferably AWS (SageMaker, S3, EC2, ECS, Redshift, Glue).

Responsibilities

• Lead ML initiatives; define scope, budgets, and timelines.
• Design, build, and maintain end-to-end ML pipelines from data prep to deployment.
• Monitor models in production and drive remediation for drift or degraded performance.
• Collaborate with cross-functional teams to translate model results into actionable insights.

Skills

Python

SQL

Time series

Anomaly detection

Machine learning

Education

Bachelor's or Master's in Engineering/CS/Data Science

Tools

SageMaker

Elasticsearch

Redshift

Grafana

Jupyter Notebooks

Git

Job description

Badger Meter is seeking a Senior Engineer, Machine Learning in Milwaukee, WI to lead ML initiatives across large-scale IoT data from meters, sensors, and connectivity devices. You will define scope, build end-to-end pipelines, and monitor model performance in production, working with data engineers and software teams to deliver reliable, scalable ML solutions.

The role emphasizes leadership, collaboration with cross-functional teams, and the development of predictive models and anomaly detection