Perception Engineer IV - Project Based

Autonomous Solutions•Mendon, UT

About The Position

This is a project-based employee role supporting the development, integration, and validation of ASI's autonomous reach stacker platforms. Development phases of this nature typically continue through project completion, often spanning two to four years, although scope and duration are determined by business and customer needs. The Perception Engineer IV develops and advances perception systems that enable ASI's autonomous reach stackers to understand container-yard environments, detect and track surrounding objects, identify containers and stacking locations, and safely perform material-handling operations. This role works with cameras, LiDAR, radar, GPS/GNSS, inertial sensors, and other sensing technologies to support environmental awareness, container detection, equipment tracking, precision alignment, obstacle avoidance, and autonomous task execution. As a Level IV engineer within ASI's five-level engineering structure, this position independently leads complex perception features and subsystem initiatives from technical definition through integration and field validation. The role influences perception architecture, resolves difficult cross-system problems, and provides technical guidance to other engineers while collaborating with GNC, embedded software, systems, test, and field operations teams. Broader platform strategy and organization-wide technical direction remain aligned with engineering leadership and Level V technical authorities.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, Mathematics, or a related technical field
Typically eight or more years of experience developing perception, computer vision, robotics, machine learning, sensor-fusion, or autonomous-system software
Advanced proficiency in C++ and Python
Demonstrated experience independently developing and delivering complex perception features for robotic or autonomous systems
Strong experience with computer vision, point-cloud processing, sensor fusion, object tracking, environmental modeling, or machine-learning algorithms
Experience working with cameras, LiDAR, radar, GPS/GNSS, inertial sensors, or other robotic sensing technologies
Strong understanding of coordinate systems, geometric transformations, sensor calibration, data synchronization, and three-dimensional geometry
Experience designing software interfaces and integrating perception components into complex hardware and software systems
Experience developing and optimizing real-time software for embedded or constrained computing platforms
Advanced experience working in Linux-based software-development environments
Experience with ROS, ROS2, or comparable robotics middleware
Experience developing production-quality software using version control, peer review, automated testing, continuous integration, and configuration-management practices
Ability to define meaningful performance metrics and use large datasets to evaluate perception-system performance
Demonstrated ability to diagnose and resolve complex software, sensor, computing, and system-integration issues
Ability to provide technical guidance and constructive feedback to other engineers
Strong analytical, debugging, and technical problem-solving skills
Strong written and verbal communication skills
Ability to work effectively with engineering, operations, customer-facing, and field-testing teams

Nice To Haves

Master's degree in Computer Science, Electrical Engineering, Robotics, Machine Learning, or a related discipline
Experience developing perception systems for autonomous reach stackers, container-handling equipment, industrial vehicles, heavy equipment, or mobile robots
Familiarity with container yards, ports, intermodal terminals, distribution centers, or material-handling operations
Experience with container detection, identification, pose estimation, stack-position recognition, or precision alignment applications
Familiarity with reach stacker spreaders, twistlocks, container pickup and placement, stacking workflows, or chassis-handling operations
Experience with OpenCV, Point Cloud Library, PyTorch, TensorFlow, CUDA, TensorRT, or similar technologies
Experience developing machine-learning solutions for detection, segmentation, classification, depth estimation, pose estimation, or tracking
Experience with LiDAR point-cloud registration, filtering, clustering, mapping, and object detection
Experience with multi-object tracking, occupancy grids, free-space detection, semantic mapping, or environmental modeling
Familiarity with Kalman filtering, Bayesian estimation, probabilistic robotics, or other sensor-fusion methods
Experience optimizing software for GPUs, embedded computers, or real-time systems
Experience with simulation, recorded-data replay, software-in-the-loop testing, and hardware-in-the-loop testing
Familiarity with functional safety principles and validation practices for autonomous or safety-critical systems
Experience validating autonomous systems in industrial, outdoor, low-light, high-traffic, dusty, or weather-exposed environments

Responsibilities

Design, develop, integrate, and validate advanced perception algorithms for autonomous reach stacker applications
Lead complex perception features involving object detection, classification, segmentation, tracking, obstacle detection, free-space identification, and environmental modeling
Develop capabilities for detecting and tracking shipping containers, trucks, trailers, chassis, reach stackers, personnel, buildings, fences, and other yard objects
Develop perception solutions that support container identification, stack-position recognition, pickup and placement verification, and precision vehicle alignment
Process and fuse data from cameras, LiDAR, radar, GPS/GNSS, inertial sensors, encoders, and other vehicle systems
Develop environmental models that support navigation, motion planning, container handling, stacking, loading, unloading, and yard-management workflows
Lead defined perception workstreams from requirements development through architecture, implementation, integration, testing, and release
Contribute to perception architecture and technical design decisions for autonomous reach stacker platforms
Establish technical approaches, performance metrics, and acceptance criteria for complex perception capabilities
Integrate perception software with autonomous vehicle platforms, embedded computing systems, vehicle interfaces, and supporting autonomy software
Evaluate perception performance using recorded datasets, simulation, software-in-the-loop testing, hardware-in-the-loop testing, and full-vehicle field validation
Analyze large datasets to identify false detections, missed detections, tracking failures, alignment errors, environmental limitations, and system-level edge cases
Develop and improve automated workflows for data collection, labeling, replay, regression testing, visualization, and performance analysis
Lead troubleshooting of complex issues involving sensor calibration, timing, synchronization, coordinate transformations, vehicle movement, computing performance, and system integration
Guide sensor selection, placement, mounting, configuration, calibration, and validation activities on reach stacker platforms
Optimize perception algorithms for real-time execution on embedded CPUs, GPUs, and other computing platforms
Collaborate with GNC engineers to ensure perception outputs support safe navigation, motion planning, obstacle avoidance, container approach, and precision positioning
Partner with systems engineers to define interfaces, requirements, failure responses, and operational constraints for perception subsystems
Work with test engineers and field testers to develop comprehensive validation scenarios for container yards and intermodal environments
Investigate difficult field failures and lead the development and verification of corrective actions
Conduct design reviews and code reviews while providing technical feedback to other engineers
Mentor less-experienced engineers and support improvements to engineering practices, development tools, and team standards
Communicate technical risks, system limitations, findings, and recommendations to engineering teams and leadership
Document algorithms, architectures, interfaces, assumptions, test results, technical decisions, and known system limitations
Support customer demonstrations, field deployments, acceptance testing, and troubleshooting activities as required