Vetted MATLAB Professionals

“Backend/data engineer with Amazon supply-chain experience building production serverless Python services and ETL pipelines on AWS (Lambda, API Gateway, S3, RDS, Glue). Has modernized legacy SAS jobs into Python with rigorous parity testing and phased migrations, and has delivered major SQL performance gains (minutes down to seconds) through indexing and partitioning.”

“Robotics engineer/researcher from Stanford’s Assistive Robotics and Manipulation Lab who built a custom tactile-sensing gripper system and ROS 2 data pipeline to classify the number of thin material layers grasped using a transformer-based model. Has internship experience at Motiv Space Systems building ROS 2 hardware abstractions for BLDC motor dynamics simulation, plus hands-on SLAM and manipulation work in Gazebo/MoveIt-based projects.”

“Robotics software candidate with hands-on experience building controllers for an Autonomous Underwater Vehicle, including dual-PID control in Python with state-space modeling and a planned path to LQR. Developed ROS nodes for odometry-based localization, waypoint planning, and control command publishing, validated through a custom Gazebo/ROS simulation workflow with control-metric-driven testing. Also worked on F1Tenth simulation and scan-matching localization (PL-ICP), with additional cloud deployment experience using Docker/Kubernetes and CI/CD.”

“Robotics/embedded engineer with hands-on experience building real-time control systems on RP2040 (hydroponics automation, 1-DOF helicopter stabilization) and full ROS 2 navigation stacks in simulation (URDF, TF, PID, A* in RViz/Gazebo). Demonstrates strong low-level protocol work (timing-sensitive one-wire in C) and rigorous debugging across hardware and software using UART instrumentation and oscilloscope verification, plus reproducible workflows with Docker and CI/CD (GitHub Actions/GitLab, incl. Sandia National Labs).”

“Robotics student who built a fully autonomous "Pacman" robot car using ROS 2, integrating LiDAR/IMU sensing with localization, autonomous driving, and a custom RRT + A* planner. Demonstrated practical embedded optimization on an RP2040 by balancing replanning frequency with safety via rapid collision checks.”