Machine Learning Engineer (Autonomy)

Mariana Minerals

$110K — $130K *
Manufacturing & Automotive
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Mechatronics, Robotics, Computer Science, Electrical, or related engineering discipline
  • 2+ years experience in autonomy, robotics, or embedded software, ideally for mobile robots or vehicles
  • Proficient in C++ and/or Python, with experience in robotics middleware such as ROS/ROS 2
  • Hands-on experience with sensor integration and fusion, including LiDAR, cameras, and radar
  • Working knowledge of real-time and embedded systems and their integration with actuation
  • Experience in autonomous vehicles or robotics, especially in challenging environments is preferred

Responsibilities

  • Develop autonomy software for mining vehicles focused on perception, SLAM, motion planning, and control
  • Integrate and calibrate sensing suite ensuring robustness against dust and vibration
  • Build and maintain embedded and real-time software prioritizing safety and reliability
  • Create simulations and data pipelines to test and evaluate autonomy performance
  • Conduct validation of the autonomy stack in diverse environments and debug across software-hardware boundaries
  • Collaborate with hardware and controls engineers for complete vehicle integration

Benefits

  • Hands-on role with opportunity to impact groundbreaking technology
  • Work at the forefront of autonomy in mining
  • Collaboration with cross-functional engineering teams
  • Chance to develop solutions for real-world challenges in extreme conditions
  • Access to cutting-edge tools and technologies for development
Full Job Description
Role Overview

We are hiring Machine Learning Engineers (Autonomy) to build the autonomy and sensor-integration software that lets our mining vehicles perceive, decide, and drive themselves.

In this role you will work on parts of the software and sensor integration that enables full mining autonomy - sensor fusion across LiDAR, cameras, radar, and IMU/GNSS; perception, localization, and mapping; and the autonomy stack that turns sensing into safe vehicle motion. You will carry work from architecture and algorithm design through implementation, simulation, and bench and field validation, working hand in hand with our hardware, controls, and systems-engineering teams. This is a hands-on, first-principles role for an engineer who wants to develop the world's first fully autonomous mines.

What You'll Do
  • Develop autonomy software for autonomous mining vehicles focusing on one or more area: perception, SLAM, motion planning, and control
  • Integrate and calibrate the sensing suite (LiDAR, cameras, radar, IMU, GNSS), implementing sensor fusion and time synchronization robust to dust, vibration, and corrosion-heavy mining environments
  • Build and maintain embedded and real-time software that bridges sensing, compute, and actuation, with attention to safety, latency, and reliability
  • Develop simulation, logging, and data pipelines to test autonomy behavior and drive performance against safety and availability targets
  • Build bench, rig, and field validation of the autonomy stack, debugging across the full software-hardware boundary
  • Collaborate with hardware and controls engineers to integrate sensing, compute, and actuation into a complete vehicle


What You'll Bring
  • Bachelor's degree in Mechatronics, Robotics, Computer Science, Electrical, or related engineering discipline
  • 2+ years developing autonomy, robotics, or embedded software, ideally for mobile robots or vehicles
  • Strong proficiency in C++ and/or Python, and with a robotics middleware such as ROS/ROS 2
  • Hands-on experience with sensor integration and fusion - LiDAR, cameras, radar, IMU, GNSS - and with perception, localization, or motion-planning algorithms
  • Working knowledge of real-time and embedded systems, and of the controls and software-hardware integration that drive actuation
  • Experience in autonomous vehicles, robotics, automotive, or off-highway equipment strongly preferred

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