San Francisco HQ
3 months ago

Job Overview

Job Type
FullTime
Pay
$160K – $200K • Offers Equity • Estimated Base Salary, Final salary dependent on level of experience, location, and other factors.

Job description

Salary:
USD 160,000 - 200,000 per year
Location:
San Francisco HQ, United States
Work arrangement:
On-site

Role Summary

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 own 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.

Responsibilities

  • 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
  • Lead 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

Requirements

  • Bachelor's degree in Mechatronics, Robotics, Computer Science, Electrical, or related engineering discipline
  • 5-10+ 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

About the Company

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.

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!

Role:
Staff Machine Learning Engineer (Autonomy)
Job Type:
FullTime

Company profile

Mariana Minerals

marianaminerals.com

Mariana Minerals is a software-first, vertically integrated minerals company focused on supplying the critical minerals that power modern energy, AI and defense technologies. It aims to reimagine the minerals supply chain by combining deep industry expertise with advanced software, automation and data-driven decision-making, working toward responsible mineral sourcing and supply. Its operating approach is to simplify and optimize processes before automating them for scale, and it states that it never puts speed or cost ahead of people's safety.

Funding Stage
Series B

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