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Machine Learning Engineer Jobs in Burlington, WI

Pwc

CTIO AI Engineering Manager

Milwaukee, WI

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Cliftonlarsonallen

Senior AI Engineer

Racine, WI · On-site

$98K - $134K/yr

This position blends applied machine learning, software engineering, cloud architecture, and end-to-end solution delivery. Success in this role requires a strong understanding that production AI ...

CliftonLarsonAllen

Lead ML Ops Engineer

Milwaukee, WI · On-site

$101K - $133K/yr

This role manages a team of Machine Learning Operations Engineers, oversees the end-to-end machine-learning strategy and execution, sets vision for MLOps, and ensures alignment with business goals.

Generac

Engineer II - Engine Coding

Waukesha, WI

The Engine Code Engineer II plays an important role in advancing the design and performance of ... Familiarity with machine learning and predictive analytics techniques applied to engine performance ...

Pwc

NGA AI Engineer Manager

Milwaukee, WI

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Pwc

NGA AI Engineer Senior Manager

Milwaukee, WI

$91K - $321K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven ...

Generac

Senior Engine Code Engineer

Waukesha, WI

$104K - $143K/yr

The Senior Engine Code Engineer plays an important role in advancing the design and performance of ... Familiarity with machine learning and predictive analytics techniques applied to engine performance ...

Milwaukee Tool

Controls Engineer

Menomonee Falls, WI · On-site

$83K - $108K/yr

... systems, machine learning, and AI-enabled manufacturing. This role partners with engineering ... manufacturing, quality, IT, analytics, suppliers, and global sites to turn emerging technologies ...

Milwaukee Tool

Controls Engineer

Menomonee Falls, WI · On-site

$83K - $108K/yr

... systems, machine learning, and AI-enabled manufacturing. This role partners with engineering ... manufacturing, quality, IT, analytics, suppliers, and global sites to turn emerging technologies ...

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All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Burlington, WI

Machine Learning Engineer information

See Burlington, WI salary details

$33.2K

$135.9K

$204.2K

How much do machine learning engineer jobs pay per year?

As of Jul 13, 2026, the average yearly pay for machine learning engineer in Burlington, WI is $135,918.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,100.00 and $163,600.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a Machine Learning Engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What cities near Burlington, WI are hiring for Machine Learning Engineer jobs? Cities near Burlington, WI with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Burlington, WI as of July 2026, with employment types broken down into 86% Full Time, 10% Part Time, 3% Contract, and 1% Nights. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $135,918 per year, or $65.3 per hour.
Senior ML/GenAI Ops Engineer - Milwaukee, WI

Senior ML/GenAI Ops Engineer - Milwaukee, WI

Harley-Davidson

Milwaukee, WI

$103K - $141K/yr

Full-time, Part-time

Medical, Retirement

Posted 15 days ago

Job description

Auto req ID: 49054 
Title: Senior ML/GenAI Ops Engineer - Milwaukee, WI 
Job Function: Digital 
Location: JUNEAU
Workplace Category:Onsite 
Company: Harley-Davidson Motor Company 
Full or Part-Time: Full Time 
Shift: SHIFT1 

At Harley-Davidson, we are building more than machines. It’s our passion and commitment to continue the evolution of this storied brand, and heighten the desirability of the Harley-Davidson experience. To keep building our legend and leading our industry through innovation, evolution, and emotion we need the best and brightest talent. We stand for the timeless pursuit of adventure. Freedom for the soul. Are you ready to join us?

Harley-Davidson Motor Company, founded in a humble Milwaukee backyard shed in 1903, still calls the city home. Today, its Corporate Campus includes a 4.8-acre public park—a welcoming greenspace open to all. Join our team as a Sr Data Engineer.

Job Summary:

We are looking for a skilled Sr. Data Engineer - ML & AI Operations to join our growing team. In this role, you will be responsible for designing, developing, and deploying & operationalizing machine learning and generative AI (GenAI) platforms to deliver high-impact solutions to business challenges and optimize processes. This role focuses on the operationalization and automation of machine learning and AI solutions, ensuring they are seamlessly integrated into production environments with a high degree of scalability, reliability, and compliance with ethical guidelines.

The ideal candidate will bring strong technical expertise in data engineering, a deep understanding of ML and AI DevOps best practices, and a commitment to building robust, maintainable systems.  You will lead the design, development, and scaling of data pipelines, ML infrastructure, and AI production systems that power models used across the business. If you are passionate about creating and operationalizing transformative ML and AI solutions, we’d love to hear from you!

 
Key Responsibilities:
Platform Design & Development:

  • Design, develop, and maintain scalable platforms for machine learning and GenAI, supporting end-to-end processes from data ingestion to model deployment and monitoring.
  • Lead end-to-end solution design for ML/AI data pipelines and model-serving platforms, ensuring architectures meet scalability, reliability, and regulatory requirements.
  • Partner closely with project and program managers to establish delivery timelines, resource plans, and milestone tracking for complex, multi-team data/ML efforts.
  • Champion best practices for reproducibility, automation, observability, and governance/COE in ML/AI operational pipelines and platforms.
  • Oversee compute governance, alert monitoring and model lifecycle.

Model Deployment & Automation:

  • Implement CI/CD pipelines for automated deployment of ML and AI models to production environments.
  • Work closely with data scientists to ensure model readiness and optimization, focusing on robust deployment and monitoring.
  • Develop and manage tools for continuous monitoring and performance management of models post-deployment to identify and resolve performance drift.

Collaboration and Business Alignment:

  • Partner with data scientists, software engineers, product owners, and stakeholders to align ML and AI solutions with business goals and performance metrics.
  • Facilitate seamless integration of ML/AI systems with business processes, ensuring data accessibility, quality, and real-time insights.

Operationalization & Maintenance:

  • Ensure systems are built for scalability, maintainability, and security, adhering to best practices in ML & AI DevOps.
  • Implement monitoring solutions to proactively address any issues in data, model performance, or infrastructure.
  • Drive architectural reviews, design decisions, and engineering standards that support long-term operational excellence for ML/AI workloads.
  • Serve as the primary technical escalation point for delivery risks and system performance issues, ensuring timely resolution and stakeholder alignment.

Ethics and Compliance:

  • Integrate AI ethics and compliance considerations into all ML/AI solutions, with a focus on data privacy, bias detection, and model transparency.
  • Implement processes to meet regulatory requirements and promote responsible AI use.

 

Education Requirements: 

  • High School Diploma or Equivalent Required
  • Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field is preferred

 

Experience Requirements: 

  • 7+ years of experience in data engineering or DevOps roles, with a focus on ML/AI platforms and infrastructure.
  • Proven experience in operationalizing and automating ML and GenAI solutions in production environments.
  • Strong experience with cloud platforms (AWS, Azure, GCP) and managing infrastructure for data and machine learning systems
  • Azure AZ-900 certification, with additional ML/LLM/RAG focused certifications preferred.

 

Technical Skills:

  • Proficiency in Azure Cloud Platform, specifically Azure ML Studio and Azure AI Foundry
  • Proficiency in Python, SQL, and ML/AI DevOps tools (e.g., MLflow, scikit learn, PyTorch, Kubeflow, TensorFlow Extended).
  • Experience with CI/CD tools (e.g., Jenkins, GitLab CI) and containerization/orchestration tools (Docker, Kubernetes).
  • Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and data pipeline tools (e.g., Apache Airflow, dbt).
  • Proficiency with vector databases, LLM workflows, or RAG pipelines.
  • Familiarity with cost management, autoscaling, and GPU governance in Azure ML.
  • Experience with data governance frameworks and security best practices.


Key Skills and Competencies

  • Technical Acumen: Strong knowledge of ML/AI lifecycle management, MLOps practices, and data pipeline optimization.
  • Collaboration & Communication: Excellent teamwork skills with an ability to work closely with cross-functional teams and communicate complex technical concepts effectively. Help influence alignment across teams.
  • Problem-Solving: Proactive approach & proven ability to identifying and solve issues in model performance, data quality, and infrastructure bottlenecks.
  • Ethics and Compliance: Deep understanding of responsible AI practices, including bias detection, explainability, and data privacy.
  • Governance & Data Integrity: Ability to enforce data privacy, lineage, and data quality controls across ML workflows, ensuring compliance with enterprise and regulatory requirements.

The pay range shown represents the national average pay range for this role. Your pay may be more or less than the stated range and is dependent on your geographic location and level of experience.

We offer an inclusive compensation package for all full-time salaried employees including, but not limited to, annual bonus programs, health insurance benefits, a 401k program, onsite fitness centers and employee stores, employee discounts on products and accessories, and more. Learn more about Harley-Davidson here.

Applicants must be currently authorized to work in the United States.

Direct Reports: No  
Travel Required: 0 - 10%  
Pay Range: 100,200 155,400 
Visa Sponsorship: This position is not eligible for visa sponsorship or visa transfer  
Relocation: This position is eligible for domestic relocation assistance (within posted country)

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