Warren, MI, US
Only PhD candidates are considered for this position. ****Please NO Masters degree ********
Machine Learning Engineer (PhD Level Only)
Job Summary
We are seeking a highly motivated Machine Learning Engineer with strong expertise in machine learning, deep learning, and image processing. This role is intended exclusively for PhD graduates (or candidates near completion) from reputable universities. Candidates with a strong academic research background in machine learning, computer vision, or related fields are encouraged to apply. Research experience during PhD will be considered equivalent to professional experience.
Key Responsibilities
Design, develop, and optimize machine learning and deep learning models, with emphasis on computer vision and image processing applications.
Conduct experiments, evaluate model performance, and iterate on algorithmic improvements.
Work with large-scale datasets for training, validation, and testing of ML models.
Translate research concepts into scalable, production-ready solutions.
Collaborate with cross-functional engineering and research teams to integrate ML models into real-world systems.
Document methodologies, experiments, and technical findings clearly for internal and external use.
Required Qualifications
PhD in Computer Science, Electrical Engineering, Robotics, Artificial Intelligence, or a closely related field from a reputable institution.
Strong foundation in machine learning, deep learning, and computer vision.
Experience with Python and machine learning frameworks such as PyTorch or TensorFlow.
Solid understanding of mathematics including linear algebra, probability, statistics, and optimization.
Demonstrated research experience in ML/AI (academic publications or thesis work acceptable in place of industry experience).
Preferred Qualifications
Publications in top-tier ML, computer vision, or AI conferences/journals.
Experience transitioning ML models from research to production environments.
Familiarity with GPU programming and performance optimization techniques.
Experience handling large-scale or real-world datasets.
