Convolutional Neural Networks Developer Jobs

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1d ago

Machine Learning Engineer II

Milwaukee Electric Tool Corporation Brookfield, WI

Brookfield, WI

1d ago

Full time Python AI Ai Ml

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Job Description

In Brookfield, Wisconsin, Milwaukee Electric Tool Corporation is expanding its machine learning capabilities to power intelligent features across its power tool lineup on a global scale. The Machine Learning Engineer II will design, validate, and refine models for power tool solutions, collaborate with cross-functional teams to deploy ML across Milwaukee products worldwide, and own projects with strong problem-solving, communication, and project-management skills.

Responsibilities

Create, advance, and validate machine learning models.
Collaborate with diverse cross-functional teams to deliver power tool solutions that impact users.
Explore and deploy new machine learning approaches within Milwaukee products worldwide, while demonstrating strong problem-solving, critical thinking, and the ability to thrive under pressure in a dynamic environment.
Maintain strong technical communication and foundational project management capabilities.
Show initiative and ownership for projects and tasks, understanding how they connect to broader initiatives.

Requirements

Completed coursework or specialization in machine learning and/or data science.
At least one year of hands-on experience applying machine learning principles and algorithms in contexts such as embedded systems, edge computing, or signal processing.
Proven experience applying core machine learning algorithms and techniques outside coursework, including unsupervised or supervised learning, classification/regression, dimensionality reduction, and model optimization.
Experience with machine learning and AI methods such as CNNS, transformers, or computer vision.
Proficient in developing and debugging code in Python.
Strong Python proficiency with extensive experience using libraries such as NumPy, pandas, scikit-learn, and Matplotlib.
Experience with at least one deep learning framework (e.g., PyTorch or Tensor Flow).
Solid foundation in statistics, linear algebra, calculus, and optimization.
Experience with modern software development workflows and version control tools.
Excellent problem-solving and critical-thinking abilities, with the ability to work well under pressure in a dynamic environment.
Strong technical communication skills and foundational project management abilities.
Proven ownership of projects or tasks and understanding of their relationships to other work.
Ability to travel up to 10 percent of the time (domestic and international).

Technologies

Python
NumPy
pandas
scikit-learn
Matplotlib
CNNS
transformers
computer vision
PyTorch
Tensor Flow
C
C++

Benefits

Health, dental and vision insurance
401(k) savings plan
Education assistance
On-site wellness, fitness center, food, and coffee service

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

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