1

Machine Learning Engineer Quantization Jobs in Milwaukee, WI

Relevant certifications such as AWS Certified Machine Learning - Specialty, Google Professional Machine Learning Engineer, or Microsoft Certified: Azure AI Engineer Associate. Work Experience

Senior AI Engineer - SFL Scientific

Milwaukee, WI · On-site

$103K - $141K/yr

Work You'll Do As a Senior AI Engineer, you'll work cross-functionally with data scientists, machine learning engineers, project managers, and industry experts to develop robust AI infrastructure and ...

AI Solutions Engineering Delivery Lead

Milwaukee, WI · On-site

$101K - $133K/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 ...

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams. * Troubleshoot ...

Showing results 41-60

Machine Learning Engineer Quantization information

See Milwaukee, WI salary details

$31K

$126.9K

$190.6K

How much do machine learning engineer quantization jobs pay per year?

As of Sep 9, 2026, the average yearly pay for machine learning engineer quantization in Milwaukee, WI is $126,869.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,000.00 and $152,700.00 per year, depending on experience, location, and employer.

What does a machine learning engineer quantization do?

A Machine Learning Engineer specializing in quantization focuses on optimizing machine learning models by reducing their size and computational requirements without significantly sacrificing accuracy. This involves converting model parameters and computations from high-precision formats (like 32-bit floating point) to lower-precision formats (such as 8-bit integers). Quantization enables faster inference, lower memory usage, and allows models to run efficiently on edge devices and mobile platforms. These engineers work closely with data scientists and hardware teams to implement, test, and validate quantized models in production environments.

What are some common challenges machine learning engineers face when implementing quantization techniques in production models?

Machine Learning Engineers working on quantization often encounter challenges such as balancing reduced model size and computational efficiency with maintaining acceptable accuracy levels. Adapting quantization methods to different hardware platforms can also require significant testing and optimization. Additionally, engineers must frequently address compatibility issues with existing deployment pipelines and ensure that quantization-aware training is properly integrated to minimize performance degradation. Collaboration with hardware and software teams is essential to streamline deployment and achieve optimal results.

What are the key skills and qualifications needed to thrive as a machine learning engineer quantization, and why are they important?

To thrive as a Machine Learning Engineer Quantization, you need a solid background in machine learning, deep learning, and computer science, typically supported by a degree in a related field. Familiarity with quantization techniques, frameworks such as TensorFlow Lite or PyTorch, and experience with hardware accelerators are crucial. Strong problem-solving skills, attention to detail, and effective collaboration set top performers apart. These capabilities are vital for efficiently deploying high-performing models on resource-constrained devices and ensuring scalable, real-world AI solutions.

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

AspectMachine Learning Engineer QuantizationData Scientist
Required CredentialsBachelor's or master's in CS, ML, or related; certifications in ML or AIBachelor's or master's in statistics, CS, or related; certifications in data analysis or statistics
Work EnvironmentDeveloping optimized ML models, deploying quantized models for efficiencyAnalyzing data, building predictive models, interpreting results
Industry UsageTech companies, AI hardware firms, embedded systemsFinance, healthcare, marketing, research institutions

Machine Learning Engineer Quantization focuses on optimizing ML models for deployment efficiency, often working closely with hardware and software teams. Data Scientists analyze data and build models for insights. While both roles require ML knowledge, quantization engineers specialize in model compression techniques, whereas data scientists focus on data analysis and interpretation.

What are popular job titles related to Machine Learning Engineer Quantization jobs in Milwaukee, WI?

For Machine Learning Engineer Quantization jobs in Milwaukee, WI, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer Quantization jobs in Milwaukee, WI look for?

The top searched job categories for Machine Learning Engineer Quantization jobs in Milwaukee, WI are:

What cities near Milwaukee, WI are hiring for Machine Learning Engineer Quantization jobs?

Cities near Milwaukee, WI with the most Machine Learning Engineer Quantization job openings:

Principal Data & AI Architect

Waukesha, WI • On-site

Generac
Electrical Equipment, Appliance, and Component Manufacturing • 1 - 10 employees

Full-time

Posted 22 days ago


Key responsibilities

  • Design, develop, and implement Data & AI architectures, including enterprise data solutions, machine learning models, and generative AI systems.

  • Define technical strategies and roadmaps for Data & AI projects, ensuring alignment with business objectives.

  • Collaborate with data scientists, data engineers, and product teams to integrate Data & AI solutions into production environments.


Generac Power Systems rating

7.0

Company rating: 7.0 out of 10

Based on 66 frontline employees who took The Breakroom Quiz

358th of 499 rated machine equipment manufacturers


Job description

We believe power is a promise - a shared commitment to be there for others when it matters most.

For more than 65 years, we've turned big ideas into solutions that help protect homes, strengthen businesses and build a more resilient, efficient, sustainable energy future.


Ready to Power a Smarter World with us?


The Principal Data & AI Architect will be responsible for designing, developing, and implementing advanced Data & AI solutions and architectures that align with the company's strategic goals and objectives.

This role requires deep technical expertise, leadership, and the ability to collaborate across cross-functional teams to deliver scalable and innovative AI solutions.

Major Responsibilities :

  • Lead the design and development of Data & AI architectures, including designing and architecting Enterprise data/MDM solutions, machine learning models, deep learning frameworks, and generative AI systems. Responsible for analyzing, implementing and deploying these solutions both on-premises and in the cloud.

  • Define technical strategies and roadmaps for Data & AI-driven projects, ensuring alignment with business objectives.

  • Collaborate with data scientists, data engineers, business functional and product teams to integrate Data & AI solutions into production environments.

  • Advice, oversee the evaluation and adoption of Data & AI technologies, tools, and platforms.

  • Serve as the technical leader and mentor to Data & AI and engineering teams.

  • Deliver scalable, secure, and optimized Data & AI solutions.

  • Lead Data & analytic literacy of the organization and serve as a translator of deep technical concepts into simple business vernacular.

  • Be aware and lead industry trends and advancements in Data & AI to help maintain a competitive edge.

  • Communicate complex technical concepts to non-technical stakeholders effectively.

This is an individual contributor role.

Minimum Job Requirements:

Education

Advanced degree (Master's or Ph.D.) in Computer Science, Artificial Intelligence, Machine Learning, or a related field also acceptable.

Work Experience

  • 8 or more years of experience in AI, machine learning, or data science, with at least 4 years in a senior or lead architect role.

  • Proven track record of designing and deploying large-scale Data & AI systems in production environments.

  • Experience leading cross-functional teams in the delivery of complex AI projects.

  • Hands-on experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and AI frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

Knowledge / Skills / Abilities

  • Must be deeply curious, desire to experiment.

  • Expertise in machine learning algorithms, Neural networks, Genetic Algorithms, Decision trees, Business dynamic models, Agent based models, Advanced statistical techniques and operations research.

  • Strong proficiency in programming languages such as Python, R, or Java.

  • Ability to design scalable, secure, and efficient AI architectures.

  • Exceptional problem-solving and analytical skills.

  • Strong leadership and mentorship abilities, with a focus on fostering innovation and collaboration.

  • Excellent communication skills, capable of translating technical concepts to diverse audiences.

  • Ability to work in a fast-paced, dynamic environment and manage multiple priorities.


Preferred Job Requirements:

Education

  • Ph.D. In Operations Research, Data Science, or a closely related field.

  • Master's degree in a relevant field with significant research or project work in AI or machine learning.

Certification / License

  • Relevant certifications such as AWS Certified Machine Learning - Specialty, Google Professional Machine Learning Engineer, or Microsoft Certified: Azure AI Engineer Associate.

Work Experience

  • Experience with generative AI and reinforcement learning.

  • Knowledge / Skills / Abilities

  • Publications or patents in AI, machine learning, or related fields.

  • Familiarity with DevOps practices and MLOps pipelines for AI deployment.

  • Experience in industries such as healthcare, finance, or technology.

Physical Requirements and Working Conditions

While performing the duties of this job, the employee is regularly required to talk and hear; and use hands to manipulate objects or controls. The employee is regularly required to stand and walk. On occasion, the incumbent may be required to stoop, bend, or reach above the shoulders. The employee must occasionally lift up to 25 pounds. Specific conditions of this job are typical of frequent and continuous computer-based work requiring periods of sitting, close vision, and the ability to adjust focus. Occasional travel.

"We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, disability status, protected veteran status, or any other characteristic protected by law."


What Generac Power Systems employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Generac logo

About Generac

Sourced by ZipRecruiter

Industry

Electrical equipment, appliance, and component manufacturing and engine, turbine, and power transmission equipment manufacturing

Company size

1 - 10 Employees

Headquarters location

Eagle, WI, US

Social media