Convolutional Neural Networks Engineer 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

The Machine Learning Engineer II role focuses on designing, developing, and validating machine learning models for power tool solutions, collaborating with cross-functional teams to deploy ML features across Milwaukee products globally, and owning projects that require strong problem-solving, communication, and project-management skills. This onsite position is based in Brookfield, Wisconsin.

Responsibilities

Design, implement, and validate machine learning models tailored to power tool applications.
Collaborate with highly cross-functional teams to deliver solutions that enhance user outcomes.
Explore and evaluate new machine learning approaches for deployment within Milwaukee products worldwide, demonstrating strong problem-solving, critical thinking, and composure in a dynamic environment.
Communicate technical concepts effectively and exercise fundamental project management capabilities.
Exhibit proactive ownership of projects and tasks, with an understanding of their links to broader initiatives.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
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 involving embedded systems, edge computing, signal processing, or related areas.
Proven experience applying fundamental machine learning algorithms in non-coursework settings, 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.
Proficiency in Python and debugging, with extensive use of libraries such as NumPy, pandas, scikit-learn, and Matplotlib.
Experience with at least one deep learning framework, such as PyTorch or TensorFlow.
Strong mathematical foundation in statistics, linear algebra, calculus, and optimization.
Experience using modern software development tools and version control systems.
Excellent problem-solving abilities, critical thinking, and capability to work under pressure in a dynamic environment.
Strong technical communication skills and fundamental project management abilities.
Demonstrated ownership of a project or tasks and understanding of their relationships to other initiatives.
Ability to travel up to 10 percent of the time, domestically and internationally.

Technologies

Python
NumPy
pandas
scikit-learn
Matplotlib
CNNs
transformers
computer vision
PyTorch
TensorFlow
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 a related field is preferred.
Three or more years of hands-on experience applying machine learning principles and algorithms involving embedded systems, edge computing, signal processing, or related fields; an advanced degree may count toward part of this experience.
Experience with time series modelling, including domains such as NLP, SLAM, forecasting, or audio/video processing.
A proven track record of developing, deploying, and implementing AI or ML solutions aligned with business objectives.
Proficiency in developing and debugging code in an embedded environment using C or C++.
Working knowledge of sensor technologies (e.g., IMU, thermistors, magnetic and optical sensors) and interfacing to microcontrollers.
Understanding of embedded systems architecture (hardware and software), including microcontroller design and operation.
Experience with diverse data collection methods and their application in relevant environments.
Experience developing and deploying machine learning algorithms to edge environments.
Proven ability to develop robust MLOps pipelines ensuring efficient deployment, monitoring, and scaling of ML models.

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