Senior Engineer- Machine Learning

Badger Meter - Where Every Drop Counts and So Do You
At Badger Meter, we're more than a leading global water technology company - we're innovators with a mission: to preserve and protect the world's most precious resource. For over 120 years, our trusted solutions have enabled our customers to optimize the delivery and use of water, maximize revenue and reduce waste.
Every employee at Badger Meter is an important part of our success. Here, your work doesn't just move a business forward - it shapes a more sustainable future. We are committed to building a workplace where we celebrate differences, empower voices, and encourage fresh ideas that drive innovation.
When you join us, you'll find:
Purpose-driven work that makes a real difference in communities around the globe.
Career growth and development opportunities designed to help you achieve your potential.
A supportive, inclusive culture where collaboration and creativity thrive.
Be part of something bigger. At Badger Meter, your contributions will ripple far beyond the workplace - creating lasting change for people and the planet.
What You Will Contribute:
Senior Engineer, Machine Learning
Job Description
The Senior Engineer, Machine Learning is a mid-career position of independence and ownership of large investigations and projects. The Senior Engineer will define the scope and tasks for machine learning initiatives and may perform the efforts themselves or with one or more engineers assisting. The successful candidate will possess strong knowledge of modeling techniques and system performance, and be able to define resources and time needed to estimate project efforts. This role focuses on the design, development, testing, validation, deployment, and ongoing tuning of machine learning pipelines and models that support product performance, installation quality, and outage detection, as well as fleet-wide health monitoring of the deployed population of millions of meters, sensors, radios, connectivity equipment, and other IoT devices in support of the Systems Integration team, and also encompasses the data infrastructure, integration, and monitoring systems that keep those models reliable at scale. The Senior Engineer is expected to contribute quickly to the team's portfolio of algorithms, ML models, and AI tools, creating predictive models, classifiers, time series data mining, and anomaly detection algorithms, and will work closely with developers, data engineers, data scientists, and reliability, quality, and design engineering to identify and address product weaknesses. The Senior Engineer will represent the team on projects and investigations, report findings to leadership, and act as a mentor to other engineers.
ESSENTIAL JOB DUTIES:
Leadership & Scope-Setting
Act as project lead engineer for machine learning initiatives; may lead a technical team
Define the scope of machine learning projects and investigations; develop or provide input to schedules and budgets
Define and control model design standards and validation criteria
Mentor other engineers, including guidance on data ingestion, cleaning, and model development practices
Provide oversight of model performance dashboards, ML-driven product support issues, and customer-impacting model behavior
ML Pipeline & Model Lifecycle
Design, build, and maintain end-to-end machine learning pipelines, from data preparation through training, validation, deployment, and production monitoring
Develop, tune, and maintain predictive models, classifiers, and AI tools, such as installation quality and outage detection models, to improve accuracy and reliability
Develop time series data mining and anomaly detection algorithms to monitor the health and performance of millions of deployed meters, sensors, radios, and IoT devices
Recommend corrective actions and trigger field responses, or hand off findings to design and reliability engineers
Establish testing and validation frameworks to confirm model outputs against real-world data, including coordinating field or manual verification studies
Monitor deployed models for drift, degraded performance, or unexpected outcomes, and lead remediation efforts
Support integration of model outputs into customer-facing dashboards and reporting tools
Identify opportunities to improve existing infrastructure, workflows, products, and investigations with machine learning models
Data Infrastructure & Integration
Design and maintain data pipelines that integrate internal and external data sources, such as carrier network data, weather data, and manufacturing test data, into shared indices and datasets
Interface with AWS cloud infrastructure for machine learning workloads, such as SageMaker, EC2, ECS, and Glue
Utilize datastores such as Elasticsearch and Amazon Redshift, including indices and data structures supporting model training and inference
Contribute to database and data architecture improvements as needed to support growing model and pipeline complexity
Cross-Functional Collaboration
Partner with engineering, digital engineering, and analytics teams to align model outputs with product and business needs
Work closely with developers, data engineers, data scientists, and reliability, quality, and design engineering to identify and address product weaknesses revealed by fleet and field data
Share standardized machine learning libraries, tools, and best practices across teams to reduce duplicated effort and improve consistency
Collaborate with customer-facing teams to validate model results and translate findings into actionable insights
Documentation & Process
Initiate and manage tickets in the team's ticketing system (e.g., JIRA)
Write, review, validate, and audit procedures related to model development, testing, and deployment
QUALIFICATIONS:
Education
Bachelor's or Master's degree in Engineering, Mathematics, Statistics, Computer Science, Data Science, or Data Analytics, or equivalent practical experience
Required Experience & Technical Skills
5+ years of related experience in machine learning, data science, or applied analytics; a Master's degree with related research may count toward a portion of this experience, depending on the topic and exposure to distributed sensor and device network data
Strong foundation in engineering and statistical fundamentals
Proficiency in Python and SQL; experience with NoSQL data stores, Elasticsearch, Amazon Redshift, Grafana, and Jupyter Notebooks; additional languages (e.g., C#) a plus
Experience with version control tools and workflows (e.g., GitHub) for code and model management
Experience with cloud-based data and machine learning platforms, preferably Amazon Web Services (e.g., S3, SageMaker, Redshift, EC2, ECS, Glue)
Significant experience with statistics, machine learning, or other mathematical modeling and simulation techniques, including predictive modeling, classification, time series data mining, and anomaly detection
Experience modeling performance and detecting anomalies in IoT device, sensor, or endpoint data at scale (e.g., fleets of meters, radios, or connectivity equipment)
Experience with large-scale time series data sets and near-real-time analysis
Experience designing, testing, validating, and deploying machine learning models in a production environment
Experience with data visualization and business intelligence (BI) tools
Ability to work with non-technical stakeholders to define expectations and success criteria for new models and algorithms, and to communicate results in clear, non-technical terms
Preferred Qualifications
Ability to quickly develop working knowledge of metering, sensor, radio, and connectivity products
Ability to independently solve problems and implement solutions
Demonstrated judgment and decision-making within a defined level of authority
Demonstrated ability to drive projects to completion
Experience integrating external or third-party data sources (e.g., carrier network data, weather data, manufacturing/test data) into machine learning pipelines
Experience managing machine learning pipelines end-to-end, from training through deployment and production monitoring
Understanding of device hardware, reliability, or failure analysis sufficient to translate model findings into corrective-action recommendations (e.g., using field returns or test data)
Familiarity with Lean Six Sigma or other continuous improvement methodologies
Competitive Total Rewards at Badger Meter:
Competitive