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About This Role
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
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QUALIFICATIONS:
Education
Bachelor's or Master's degree in Engineering, Mathematics, Statistics, Computer Science, Data Science, or Data Analytics, or equivalent practical experience
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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
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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 Pay
- Annual Bonus
- Eligible for Annual Pay Increases
- Comprehensive Health, Vision, and Dental Coverage
- 15 days Paid Time Off + 11 Paid Holidays
- Two Ways to Save for Retirement: Badger Meter contributes 25 cents for every dollar you contribute to the plan, up to 7% of your eligible compensation. In addition to the match, the company will also contribute 5% of your eligible compensation to your Defined Contribution account on an annual basis. Additional access to a certified financial planner to help ensure your money is working for you, at no cost!
- Employer Paid benefits including: Employee Assistance Program (EAP), Basic Group Life Insurance, Short Term Disability, and more
- Educational Assistance – Tuition Reimbursement up to $5,250
- Voluntary benefits including : Additional Life Insurance, Long Term Disability, Accident and Critical Illness coverage
- Health Savings Account (HSA) & Flexible Spending Account (FSA) options
An Equal Opportunity/Affirmative Action Employer. This company considers candidates regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status. Applicants can learn more about their rights regarding equal opportunity in employment by viewing the federal "EEO is the Law" poster and the “EEO is the Law” poster supplement at http://www.dol.gov/ofccp/regs/compliance/posters/ofccpost.htm
Badger Meter complies with all aspects of the Americans with Disabilities Act (ADA), as amended by the ADA Amendments Act, and all applicable state or local disability laws. This means that we will reasonably accommodate qualified employees with a disability if accommodation would allow them to perform the essential functions of their job, unless doing so would create an undue hardship.
Privacy Statement
The Employee and Applicant Privacy Statement describes how we collect, use, share, retain, and safeguard applicant information. Please see the privacy statement on our website here .
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Badger Meter, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Badger Meter AI Hiring
Badger Meter has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Milwaukee, WI, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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