Machine Learning Engineer II

Techtronic Industries - TTI•Anderson, SC

5d•Onsite

About The Position

TTI Consumer Power Tools, Inc., has an opening in its Anderson, SC corporate office for a Electrical Machine Learning (ML) Engineer II that will report into the Sr. Director of Engineering. Come join the Engineering team at the most exciting and innovative tool company in the world! Techtronic Industries is a fast-growing world leader in Power Tools, Accessories, Hand Tools, Outdoor Power Equipment, and Floor Care for Do-It-Yourself (DIY). The Company is committed to accelerating the transformation of these industries through superior environmentally friendly cordless technology. At TTI Anderson, our RYOBI brand is recognized worldwide for their deep heritage and cordless product platforms of superior quality, outstanding performance, safety, productivity, and compelling innovation. Your Role On Our Team: The Electrical ML Engineer II will identify, develop, and validate ML-enabled features for outdoor power tools within the OP EE organization. This role partners closely with cross-functional teams to translate customer needs into robust, data-driven solutions that can be demonstrated on-tool and matured toward production readiness. Key responsibilities include framing ML use cases, designing data-collection strategies and ground-truth methods, performing data engineering and feature development, training and tuning models in Python, and executing disciplined validation to characterize accuracy, robustness, and corner cases. The engineer will also help establish and improve team workflows and guidelines covering the end-to-end ML lifecycle—from problem definition through model training, validation, and deployment considerations.

Requirements

Bachelor of Science degree in Computer Science, Computer Engineering, Electrical Engineering, or a related scientific/engineering discipline with completed coursework, certification, or specialization in Machine Learning, Data Science, or a closely related area.
Minimum 2 years of hands-on experience applying ML in an applied setting (e.g., embedded/edge, signal processing, sensing, or electromechanical products), including data collection and model validation.
Demonstrated experience applying fundamental ML approaches (e.g., supervised learning for classification/regression, feature engineering, model selection, and hyperparameter tuning).
Proficient in developing, debugging, and validating Python code, with experience in common libraries (NumPy, pandas, scikit-learn, Matplotlib, etc.).
Experience with modern software development practices and tools (Git-based workflows; familiarity with CI/CD and containers is a plus).
Strong problem-solving and system-thinking skills, with the ability to work in a fast-paced, prototype-to-demo environment.
Strong technical communication skills and the ability to plan/execute work to meet project schedules.
Demonstrated ownership of deliverables, including understanding dependencies and collaborating effectively with cross-functional stakeholders.

Nice To Haves

Ability to develop and debug code in an embedded environment (C/C++ preferred), including bring-up and hardware-facing troubleshooting will be a plus.
Basic knowledge of sensor technologies (e.g., IMUs, thermistors/temperature sensing, magnetic/optical sensing) and microcontroller interfacing.

Responsibilities

framing ML use cases
designing data-collection strategies and ground-truth methods
performing data engineering and feature development
training and tuning models in Python
executing disciplined validation to characterize accuracy, robustness, and corner cases
help establish and improve team workflows and guidelines covering the end-to-end ML lifecycle—from problem definition through model training, validation, and deployment considerations