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Mariana Minerals

Machine Learning Engineer (Autonomy)

Location
San Francisco, CA, US
Arrangement
Hybrid
Employment type
Full-time
Posted
21 August 2026 (about a month ago)

Checked 26 days agoApplications go to the employer, never to RoleSprint

About this role

ABOUT MARIANA MINERALS

Mariana Minerals is a software-first, vertically integrated minerals company on a mission to supply the critical minerals powering modern energy, AI, and defense technologies. We’re reimagining the minerals supply chain by combining deep industry expertise with advanced software, automation, and data-driven decision-making.

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

OUR CULTURE IS BUILT ON FOUR PRINCIPLES:

Everyone Gets Home Safe. We never put speed or cost ahead of people.

Extreme Ownership. We take full responsibility for outcomes, relentlessly driving toward solutions.

Engineer Out Requirements, then Automate. We simplify, optimize, and then automate for scale.

Share Your Legos. We collaborate openly, share knowledge, and empower each other to build bigger, better solutions.

Join us as we build the future of responsible mineral sourcing and supply!

Locations

  • San Francisco, CA, US
  • Houston, TX, US
  • Ann Arbor, MI, US

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About this listing

Advertised by Marianaminerals and published on Ashby, the applicant tracking system they use.

RoleSprint is not the employer and not a recruiter. Applications are made on the employer’s own site and never reach us; what RoleSprint does is help you decide whether a role is worth your time and prepare for it if it is.

Published 21 August 2026, last checked 26 days ago. A posting stops being advertised here 90 days after the employer published it, and one the employer takes down is marked closed rather than quietly removed.

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