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Machine Learning Engineer Quantization Jobs in Milwaukee, WI

Bespoke Labs

Machine Learning Engineer

Kenosha, WI · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Milwaukee, WI · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Racine, WI · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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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 Jul 29, 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 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 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 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:
Machine Learning Engineer Quantization jobs near you

Machine Learning Engineer I

Milwaukee Tool

Brookfield, WI • On-site

Full-time

Re-posted 14 days ago

Job description

Job Summary:
Techtronic Industries - TTI is committed to innovation and excellence in engineering, particularly in the realm of power tools. As a Machine Learning Engineer I, you will lead the deployment of machine learning models, collaborating with cross-functional teams to enhance product development and implementation. This role emphasizes problem-solving, technical communication, and project management to drive initiatives that improve user experiences.
Responsibilities:
• Deploy machine learning models in creative ways while working with cross-functional teams.
• Act as a technical expert in the creation and execution of concepts into products.
• Support the team through implementation, validation, and transfer to production.
• Leverage strong technical communication skills and project management abilities to ensure clarity and alignment across teams.
• Demonstrate a strong sense of ownership for projects and tasks, understanding their connection to broader initiatives.
Qualifications:
Required:
• 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
• 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
• Ability to travel up to 10% of the time (domestic and international).
Preferred:
• Master’s degree or PhD in Machine Learning or related field
• 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 are preferred
• Experience with time series modelling, especially with related domains such as NLP, SLAM, forecasting, or audio/video processing
• Proficient developing and debugging code in an embedded environment in a programming language such as C or C++
• Experience working with modern software development tools and version control tools
Company:
Milwaukee Tool manufactures electric power tools and accessories. Founded in 1924, the company is headquartered in Brookfield, USA, with a team of 5001-10000 employees. The company is currently .

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