Computer Vision Engineer

At Kargo, our mission is to build a connective tissue between the physical world of freight and the digital ecosystem used to manage it. We believe that advancements in smart infrastructure are critical to enabling a safer and more efficient future for logistics. Our loading dock sensor platform verifies all incoming and outgoing freight, aggregating data that enables shippers and carriers to efficiently manage dock operations, switch out suppliers and understand material flow in real time.

We care deeply about delivering the best solutions for our customers. As a member of the Kargo team, you will have the opportunity to develop and deploy tomorrow’s hardware & software solutions and help revolutionize logistics.

As a CV/ML Engineer at Kargo, you will be a technical contributor on the engineering team with deep expertise in building and deploying scalable machine learning. You will work closely with our hardware and software engineering teams to build and deploy powerful computer vision features for our customers.

Responsibilities

• Build CV/ML algorithms for various tasks around vision, including training and optimizing CV/ML models.
• Build the backend or edge infrastructure to scale our training and inference workload, including training pipelines, evaluation, and model deployment.
• Design and collect datasets, and train models. Convert data to models and drive specs for data.

Experience

• 3+ years experience as a Computer Vision Engineer, Machine Learning Engineer, or similar role.
• Experience in Python, PyTorch (Java, C++ are pluses).
• Experience building and deploying large-scale machine learning models with feedback loops for continuous improvement.
• Experience building performant, distributed training and inference pipelines on very large datasets.
• Comfortable with full-stack / backend development code to build a strong understanding of underlying data structures and other dependencies.
• Experience building, and optimizing CV models or algorithms on the edge.
• Degree in Computer Science, Math, Statistics, Engineering, or a related quantitative field, or equivalent experience.