AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit www.aerovect.com.
Build and maintain data pipelines to ingest field data (rosbags, sensor logs, telemetry) from our fleet
Convert raw field data into curated, versioned datasets for the perception team and own dataset management - storage, indexing, querying, and vending datasets
Set up training workflows and optimize cloud costs
Build tooling to accelerate perception engineers' workflows - fast data access, reproducible experiments, automated evaluation pipelines
Generate metrics and diagnostics to track dataset health, model performance, and pipeline reliability
Bachelor's or Master's degree in Computer Science, Robotics, Data Engineering, or a related field
Strong Python proficiency and working knowledge of ROS2
Working knowledge of docker and other DevOps tools
Familiarity with cloud storage and compute (AWS - S3, EC2, etc.)
Understanding of ML workflows and dataset versioning
Master's in Computer Science, Robotics, or a related discipline
2+ years of MLOps or data infrastructure experience, ideally in robotics or autonomous systems
Experience with Weights & Biases, rosbag data, and large-scale sensor datasets
Working knowledge of C/C++
Experience supporting perception or ML research teams
Please note this role will be based onsite in Toronto
Job details are sourced from the employer's original posting.
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AeroVect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. They are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving.