Software Engineer, ML Ops

Aerovect
Toronto, Canada
Remote

Who this role is best for

Aimed at mid-level to senior data engineers or ML ops professionals who build and optimize data and training pipelines in autonomous systems.

Best fit for

  • Mid-level to senior data engineers with experience in autonomous systems and dataset versioning
    — “Understanding of ML workflows and dataset versioning”
  • Candidates with a background in robotics or DevOps and a focus on perception systems
    — “Experience supporting perception or ML research teams”
  • Individuals who have worked with sensor data and cloud infrastructure in robotics or autonomous systems
    — “Build and maintain data pipelines to ingest field data (rosbags, sensor logs, telemetry) from our fleet”

Things to consider

  • Onsite presence in Toronto is mandatory, with no remote flexibility implied
    — “This role will be based onsite in Toronto”
  • Candidates should be prepared to work with large-scale sensor datasets and ROS2
    — “Experience with rosbag data, and large-scale sensor datasets”

How to stand out

  • Highlight experience with dataset curation and versioning in robotics or autonomous systems
    — “Convert raw field data into curated, versioned datasets for the perception team”
  • Demonstrate hands-on skills with cloud cost optimization and ML training workflows
    — “Set up training workflows and optimize cloud costs”
  • Showcase tooling development experience to support ML research teams
    — “Build tooling to accelerate perception engineers' workflows”
  • Emphasize familiarity with DevOps tools and Docker for infrastructure work
    — “Working knowledge of docker and other DevOps tools”
Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • build and maintain data pipelines
  • set up training workflows
  • build tooling for perception engineers
Typical background
MLOps or data infrastructure experienceMaster's in Computer Science, Robotics

Skills & requirements

Required

PythonROS2Cloud Storage And ComputeML WorkflowsDataset Versioning

Preferred

C/c++Weights & BiasesRosbag DataLarge-scale Sensor Datasets

Stack & domain

PythonROS2DockerCloud StorageCloud ComputeAWSS3EC2ML WorkflowsDataset VersioningWeights & BiasesRosbag DataLarge-scale Sensor DatasetsC/c++CommunicationCollaborationTechnical MentoringMlopsData InfrastructureRoboticsAutonomous Systems

About the role

Original posting from Aerovect via Ashby

WHO WE ARE

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 http://www.aerovect.com.

YOU WILL

  • 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

YOU HAVE

  • 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

WE PREFER

  • 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

Source: Aerovect careers (Ashby)

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