The setting
's lets heavy mining trucks and equipment operate on their own at customer mine sites. It combines vehicle controls, sensors, computers and fleet software.
Review speed depends on evidence that is scattered across vehicle, test, and code contexts.
Faster review protects vehicle availability, while complete evidence protects safe changes on active mine sites.
Who you’d be doing this for
“Dispatch asked why two trucks are still limited, and I’m waiting on a change nobody can clear yet.”
Olumide Mensah · Mine Operations Manager · a character in this brief
Runs production shifts at a mine site where autonomous haul trucks may remain restricted until engineering changes are cleared.
What is at stake
Mine operations managers are waiting on fixes while reviews reopen for missing vehicle evidence. You must decide whether AI marks uncertain evidence or holds the change for an engineer to investigate.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- review readiness vs. incomplete field evidence
- consistent checks vs. vehicle-specific context
- telemetry links vs. code-level test coverage
- site reporting burden vs. evidence completeness
Why Applied Intuition
Mining Autonomy and Drive-by-Wire System deployments depend on engineers proving that changes remain safe across real vehicle integrations.
Written with these in mind
Not your kind of problem? 93 more at Applied Intuition, or browse every organization.
Where the organization context comes fromThe postings this brief was written from, and the reporting read before it.
The organization context comes from public information. The situation and characters are written for the brief.
Job postings it was written from
Public descriptions of the work at Applied Intuition. They are listed as sources, not as openings. The lines under a posting are its own words, where it meets this brief.
- Vehicle Controls - Technical Lead (opens in a new tab)
- Characterize the dynamics of customer vehicles across many embodiments — haul trucks, excavators, wheel loaders, dozers, and more — to create tailored controls solutions and close the gap between simulation and real-world behavior
- We work on a variety of different vehicle platforms: haul trucks, excavators, wheel loaders, dozers, and other heavy equipment across construction, mining, and agriculture.
- Software Engineer - Control Center (opens in a new tab)
- Control Center acts as the central nervous system for autonomous operations, handling everything from continuous telemetry ingestion and real-time digital twins to mission orchestration, safety-critical geofencing, and site-wide path planning.
- As a Software Engineer on the Control Center team, you will build the central operational platform that connects autonomous vehicle fleets to human operators across a wide range of industries including mining, commercial trucking, defense, industrial automation, and consumer AVs.
- Software Engineer - Onboard Maps (opens in a new tab)
- Extend the loop offboard over time: aggregate divergence detections across fleet drive logs into map freshness and correction pipelines
- Define the map trust contracts the planner builds against: confidence semantics, degraded-mode behavior, and the operating points that hold across vehicle programs
- Software Engineer - C++ (opens in a new tab)
- Whether applying your experience to application-level onboard software in a modern C++ codebase powering on-road products or building systems that operate 200+ ton mining vehicles on deformable off-road terrain, you’ll play a key role in building the autonomy stack powering the next generation of intelligent vehicles.
- Mechatronics Integration Engineer, Vehicle OS (opens in a new tab)
- Act as the primary technical expertise for firmware/mechatronics systems: triage issues, collect logs/telemetry, reproduce problems, and coordinate fixes with the Hardware/Firmware development team while keeping customers unblocked
- Design and evaluate sensors, actuators, and communication technologies into existing vehicle systems
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.
Where similar work appears
Other live briefs connected to this one, and what connects them.
The same setting, from another role
Each is set around Applied Intuition’s Mining Autonomy and Drive-by-Wire System, written for a different role. They’re separate briefs, not the same scenario.
Older haul trucks wait in the workshop for custom autonomy installations.
You must decide what belongs in a common module and what remains truck-specific.
One haul truck leaves autonomous service when dust reaches its lidar enclosure.
You must choose what gets installed across six trucks before drilling makes the retrofit hard to undo.
One autonomous haul truck loses position on rough loaded routes.
You must weigh a tougher installation against service access and repeatable calibration.
Other problems with a similar purpose
In each of these, as in this one, the aim is to use evidence to make delivery leaner and more efficient without treating reliability as something to trade away.
Worker-type concerns are appearing repeatedly across payroll API migration feedback, but their source evidence is scattered.
You must decide where the drafting of clusters from Operations and Compliance feedback stops and where a product manager's own reading of the source examples has to begin.
Daily event-air exceptions are producing conflicting explanations for the same cost and service variance.
You must decide what AI can match and draft, and what reviewers must verify before finance acts.
Refund-policy changes are approaching review while Jira, Salesforce, and Slack disagree about unresolved work.
You must decide what AI can reconcile and what Program Managers must verify before acting.
