Data Science & Computer Vision Engineer

New York

Machine Learning

Permanent

Our client, an innovator in the automotive industry, are hiring a Data Science & Computer Vision Engineer to join the team in New York. The successful candidate will tackle exciting challenges in telematics data processing, computer vision and machine learning, and have a future opportunity work on the next generation of cutting-edge machine learning models.

Responsibilities

As the Data Science & Computer Vision Engineer, you will implement and optimize computer vision solutions and ML models for practical applications.
Collaborate with a skilled team to deploy machine learning products at scale, including image segmentation, object detection/recognition and classification.
Investigate emerging trends in computer vision, such as 3D scene reconstruction and efficient edge model deployment.
Leverage large datasets to create innovative solutions, focusing on data wrangling, model validation and robust testing.
Engage in machine learning discussions, emphasizing pragmatic solutions over theoretical debates.

Skillset

Minimum of two years of experience in machine learning, with a minimum of one year specializing in computer vision.
Solid understanding of the fundamentals of machine learning, including linear algebra, statistics, optimization and algorithms.
Hands-on experience implementing computer vision solutions and deploying machine learning products at scale.
Familiarity with supervised, unsupervised, deep learning and reinforcement learning algorithms.
Proficiency in Python, with additional experience in C++ or R a bonus.
Expertise in OpenCV, deep learning frameworks (e.g., Pytorch, TensorFlow) and on-device libraries such as CoreML.
Passion for working with large datasets and tackling complex data challenges in innovative ways.
Excellent communication skills, with the ability to present solutions to both technical and non-technical stakeholders.
Strong attention to detail and a commitment to maintaining high-quality model development, validation metrics, and datasets.