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Machine Learning Engineer Quantization Jobs in Milwaukee, WI

Bespoke Labs

Machine Learning Engineer

Racine, WI · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Kenosha, WI · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Milwaukee, WI

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Milwaukee, WI · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Racine, WI

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Kenosha, WI

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

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All JobsMachine Learning Engineer Quantization JobsMachine Learning Engineer Quantization Jobs in Milwaukee, WI

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:

Machine Learning Engineer Quantization jobs near you

Machine Learning Engineer II

Brookfield, WI • On-site

Milwaukee Tool
See more jobs at Milwaukee Toolexternal

Full-time

Medical, Dental, Vision, Retirement

Re-posted 23 days ago

Key responsibilities

  • Create, develop, and validate machine learning models.

  • Work with cross-functional teams to integrate machine learning solutions into Milwaukee products.

  • Innovate and explore new machine learning methods for deployment in power tool solutions.

Job description

Machine Learning Engineer

Job Description:

Applicants must be authorized to work in the U.S.; Sponsorship is not available for this position at this time.

At Milwaukee Tool we firmly believe that our People and our Culture are the secrets to our success - so we give you unlimited access to everything you need to create disruptive new technologies and solutions on our engineering teams. Our Engineering Team is responsible for giving life to the batteries, motors, and electronics that power solutions changing the lives of our users. Every developmental phase of these critical components happens in-house under the watch of this team. We continue to invest in engineering resources to design and develop leadership in electronic capabilities; something unique within the industry. And we're pushing the limits in firmware engineering, power electronics, embedded systems, machine learning, and the use of artificial intelligence.

Your role on our team

As a Machine Learning Engineer II, you will create, develop, and validate machine learning models while working with highly cross-functional teams to make power tool solutions that change the lives of our users. You will innovate and explore new machine learning solutions to deploy into Milwaukee products around the world while demonstrating excellent problem-solving skills, critical thinking, and the ability to thrive under pressure in a dynamic environment. Success in this role also requires strong technical communication skills and fundamental project management abilities, along with a proactive sense of ownership for projects and tasks and an understanding of how they connect to broader initiatives.

What TOOLS you'll bring with you:

  • Bachelor of Science Degree in Computer Science, Computer Engineering, Electrical Engineering or other scientific or engineering discipline.

  • Completed course work or specialization in Machine Learning and/or Data Science

  • At least one year of hands-on experience applying machine learning principles and algorithms involving embedded systems, edge computing, signal processing or a related field

  • Demonstrated experience applying fundamental machine learning algorithms and techniques in a non-coursework setting (e.g. unsupervised or supervised learning, classification/regression, dimensionality reduction, model optimization)

  • Demonstrated experience with machine learning and AI methods such as CNNS, transformers, or computer vision

  • Proficient developing and debugging code in Python

  • Proficiency in Python, with extensive experience in common libraries (NumPy, pandas, scikit-learn, Matplotlib, etc.)

  • Proficiency with at least one deep learning framework (e.g. PyTorch of Tensor Flow)

  • Sold mathematical foundation in statistics, linear algebra, calculus and optimization

  • Experience working with modern software development tools and version control tools

  • Excellent problem-solving skills, critical thinking, and ability to work well under pressure in a dynamic environment.

  • Excellent technical communication skills and fundamental project management abilities

  • Demonstrated strong sense of ownership of a project or tasks and understanding of relationships to other tasks/projects

  • Ability to travel up to 10% of the time (domestic and international).

Other TOOLS we prefer you to have:

  • Master's degree or PhD in Machine Learning or related field is preferred

  • At least three years of hands-on experience applying machine learning principles and algorithms involving embedded systems, edge computing, signal processing or a related field (an advanced degree may count toward some experience)

  • Experience with time series modelling, especially with related domains such as NLP, SLAM, forecasting, or audio/video processing

  • Proven track record of developing, deploying and implementing AI or ML solutions connected to business objectives

  • Proficient developing and debugging code in an embedded environment in a programming language such as C or C++

  • Working knowledge of various sensor technologies (e.g. IMU, thermistors, magnetic and optical) and interfacing to microcontrollers

  • Working knowledge of embedded systems architecture (HW & SW), microcontroller design and operation

  • Experience with different types of data collection methods, understanding their principles and demonstrating their value in relevant environments

  • Experience developing and deploying machine learning algorithms to edge environments

  • Demonstrated ability to develop robust MLOps pipelines and ensure efficient deployment, monitoring and scaling of ML models

We provide these great perks and benefits:

  • Robust health, dental and vision insurance plans.

  • Generous 401 (K) savings plan.

  • Education assistance.

  • On-site wellness, fitness center, food, and coffee service.

  • And many more, check out our benefits site HERE.

Milwaukee Tool is an equal opportunity employer.

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