{"id":2455718,"url":"https://alion.io/job/badger-meter-senior-engineer-machine-learning","title":"Senior Engineer- Machine Learning","company":{"id":2168200,"name":"Badger Meter","domain":"badgermeter.com","url":"https://alion.io/company/badgermeter","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Milwaukee, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":119000,"max_usd":227000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1124},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon EC2","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"C#","optional":false},{"name":"ElasticSearch","optional":false},{"name":"GitHub","optional":false},{"name":"Grafana","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Time Series Forecasting","optional":false}],"status":"live","first_seen_at":"2026-10-08T00:00:00Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-14T16:18:01Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Badger Meter - Where Every Drop Counts and So Do You\nAt 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.\nEvery 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.\nWhen you join us, you'll find:\nPurpose-driven work that makes a real difference in communities around the globe.\n\nCareer growth and development opportunities designed to help you achieve your potential.\n\nA supportive, inclusive culture where collaboration and creativity thrive.\n\nBe part of something bigger. At Badger Meter, your contributions will ripple far beyond the workplace - creating lasting change for people and the planet.\nWhat You Will Contribute:\nSenior Engineer, Machine Learning\nJob Description\nThe 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.\nESSENTIAL JOB DUTIES:\nLeadership & Scope-Setting\nAct as project lead engineer for machine learning initiatives; may lead a technical team\nDefine the scope of machine learning projects and investigations; develop or provide input to schedules and budgets\nDefine and control model design standards and validation criteria\nMentor other engineers, including guidance on data ingestion, cleaning, and model development practices\nProvide oversight of model performance dashboards, ML-driven product support issues, and customer-impacting model behavior\nML Pipeline & Model Lifecycle\nDesign, build, and maintain end-to-end machine learning pipelines, from data preparation through training, validation, deployment, and production monitoring\nDevelop, tune, and maintain predictive models, classifiers, and AI tools, such as installation quality and outage detection models, to improve accuracy and reliability\nDevelop time series data mining and anomaly detection algorithms to monitor the health and performance of millions of deployed meters, sensors, radios, and IoT devices\nRecommend corrective actions and trigger field responses, or hand off findings to design and reliability engineers\nEstablish testing and validation frameworks to confirm model outputs against real-world data, including coordinating field or manual verification studies\nMonitor deployed models for drift, degraded performance, or unexpected outcomes, and lead remediation efforts\nSupport integration of model outputs into customer-facing dashboards and reporting tools\nIdentify opportunities to improve existing infrastructure, workflows, products, and investigations with machine learning models\nData Infrastructure & Integration\nDesign 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\nInterface with AWS cloud infrastructure for machine learning workloads, such as SageMaker, EC2, ECS, and Glue\nUtilize datastores such as Elasticsearch and Amazon Redshift, including indices and data structures supporting model training and inference\nContribute to database and data architecture improvements as needed to support growing model and pipeline complexity\nCross-Functional Collaboration\nPartner with engineering, digital engineering, and analytics teams to align model outputs with product and business needs\nWork 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\nShare standardized machine learning libraries, tools, and best practices across teams to reduce duplicated effort and improve consistency\nCollaborate with customer-facing teams to validate model results and translate findings into actionable insights\nDocumentation & Process\nInitiate and manage tickets in the team's ticketing system (e.g., JIRA)\nWrite, review, validate, and audit procedures related to model development, testing, and deployment\nQUALIFICATIONS:\nEducation\nBachelor's or Master's degree in Engineering, Mathematics, Statistics, Computer Science, Data Science, or Data Analytics, or equivalent practical experience\nRequired Experience & Technical Skills\n5+ 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\nStrong foundation in engineering and statistical fundamentals\nProficiency in Python and SQL; experience with NoSQL data stores, Elasticsearch, Amazon Redshift, Grafana, and Jupyter Notebooks; additional languages (e.g., C#) a plus\nExperience with version control tools and workflows (e.g., GitHub) for code and model management\nExperience with cloud-based data and machine learning platforms, preferably Amazon Web Services (e.g., S3, SageMaker, Redshift, EC2, ECS, Glue)\nSignificant experience with statistics, machine learning, or other mathematical modeling and simulation techniques, including predictive modeling, classification, time series data mining, and anomaly detection\nExperience modeling performance and detecting anomalies in IoT device, sensor, or endpoint data at scale (e.g., fleets of meters, radios, or connectivity equipment)\nExperience with large-scale time series data sets and near-real-time analysis\nExperience designing, testing, validating, and deploying machine learning models in a production environment\nExperience with data visualization and business intelligence (BI) tools\nAbility 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\nPreferred Qualifications\nAbility to quickly develop working knowledge of metering, sensor, radio, and connectivity products\nAbility to independently solve problems and implement solutions\nDemonstrated judgment and decision-making within a defined level of authority\nDemonstrated ability to drive projects to completion\nExperience integrating external or third-party data sources (e.g., carrier network data, weather data, manufacturing/test data) into machine learning pipelines\nExperience managing machine learning pipelines end-to-end, from training through deployment and production monitoring\nUnderstanding of device hardware, reliability, or failure analysis sufficient to translate model findings into corrective-action recommendations (e.g., using field returns or test data)\nFamiliarity with Lean Six Sigma or other continuous improvement methodologies\nCompetitive Total Rewards at Badger Meter:\nCompetitive Pay\n\nAnnual Bonus\n\nEligible for Annual Pay Increases\n\nComprehensive Health, Vision, and Dental Coverage\n\n15 days Paid Time Off + 11 Paid Holidays\n\nTwo 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. Additionalaccess to a certified financial planner to help ensure your money is working for you, at no cost!\n\nEmployer Paid benefits including: Employee Assistance Program (EAP), Basic Group Life Insurance, Short Term Disability, and more\n\nEducational Assistance - Tuition Reimbursement up to $5,250\n\nVoluntary benefits including: Additional Life Insurance, Long Term Disability, Accident and Critical Illness coverage\n\nHealth Savings Account (HSA) & Flexible Spending Account (FSA) options\n\nAn Equal Opportunity/AffirmativeAction 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\nBadger 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.\nPrivacy Statement\nThe Employee and Applicant Privacy Statement describes how we collect, use, share, retain, and safeguard applicant information. 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