Contract ML Engineer, Autonomous Mining Operations

The challenge

Optimize the dispatch and path planning algorithms for a large-scale autonomous haul fleet. Improve cycle time and safety in dynamic, GPS-challenged environments.

Role overview

ML models optimizing the dispatch and routing of autonomous haul trucks in open-pit mines. Which truck goes to which shovel, which route it takes, how fast it travels -- the optimization decisions that determine whether the fleet hits its throughput targets.

Specializations

Technical requirements

• MS in Operations Research, Machine Learning, or Engineering with optimization focus
• 5+ years in ML-based optimization for physical operations (mining, logistics, manufacturing, or fleet management)
• Experience with combinatorial optimization and vehicle routing problems
• Proficiency in Python and optimization frameworks (OR-Tools, Gurobi, or CPLEX)
• Experience with geospatial data processing and terrain modeling
• Willingness to travel to mine sites (up to 30%)

Key responsibilities

• Develop dispatch optimization models for autonomous haul truck fleets (50-100+ trucks per site)
• Build predictive models for shovel loading rates, haul road conditions, and traffic congestion
• Design terrain and road condition models using LiDAR survey data and truck sensor telemetry
• Implement fleet simulation environments for testing dispatch strategies before mine deployment
• Analyze operational data to identify throughput bottlenecks and develop targeted improvements
• Support mine site teams on model deployment, monitoring, and parameter tuning

Compensation & Benefits

• Hourly rate: $80-95/hour ($166,400 - $197,600 annualized)
• 12-month contract with renewal option
• Travel and accommodation covered for mine site visits
• Remote work between site visits