Vetted Gazebo Professionals

“Robotics software engineer with deep ROS/ROS 2 experience spanning both simulation and real hardware. Led a Master’s thesis building a decentralized multi-robot control stack in ROS 2 (Isaac Sim + Nova Carter), implementing nonlinear MPC (casADI) and RRT* planning with SLAM/3D LiDAR, and tackled tricky frame-convention integration issues. Previously built an autonomous quadruped for first responders (PACRR), integrating LiDAR/RGB-D/IMU sensors and tuning navigation behavior through iterative field testing.”

“IT Analyst/research background with hands-on experience deploying and hardening a multi-agent AI support/triage system (ticket ingestion + knowledge-base retrieval) with strong emphasis on reliability and observability. Has debugged real production issues spanning backend services and network latency (sync failures/partial writes) and is comfortable in Linux environments; also has academic exposure to robotics simulation and ROS2.”

“Robotics software engineer from Bigbot who led localization and perception for an outdoor autonomous delivery robot, building ROS2/Nav2-based autonomy with EKF sensor fusion (IMU/odometry/GPS) and perception-driven dynamic costmaps. Experienced taking systems from Gazebo simulation to real-robot deployment, optimizing real-time behavior via logging-driven debugging and latency reduction, and integrating heterogeneous comms (MAVROS/MAVLink, UART/CAN, MQTT) for distributed and multi-robot setups.”

“Robotics software engineer with hands-on hardware integration who built an AI-enabled smart dog door using a Raspberry Pi, camera-based recognition (DeepFace adapted for dogs), and stepper motor control (TB6600/NEMA 17). Experienced in ROS/ROS 2 across perception-to-controls, rigorous bag-driven debugging of SLAM/navigation issues, and deploying robot software with simulation-in-the-loop testing plus Docker/Kubernetes CI/CD.”

“Robotics software engineer with experience spanning safety-critical embedded medical hardware (low-cost neonatal baby warmer with PID temperature regulation) and advanced multi-robot planning research (belief-space planning with abstraction + MCTS to handle uncertainty). Strong ROS/ROS2 practitioner (Nav2/SLAM Toolbox/MoveIt) who builds custom packages (e.g., Insta360 panoramic imaging) and is hands-on debugging real robots from SLAM/frontier exploration to multi-robot collision avoidance and real-time performance.”

“Machine learning robotics engineer with internship experience deploying object detection and semantic segmentation models to an autonomous vehicle fleet operating in airports and naval docking stations, optimizing with ONNX/TensorRT for NVIDIA Jetson edge deployment. Also built ROS/ROS2-based decentralized multi-drone coordination (TF trees, shared telemetry) validated in Gazebo and networked via Nimbro with sub-10ms latency messaging.”

“Robotics software engineer focused on production ROS 2 (Humble) systems for warehouse AMRs, with strong architecture and integration chops. Built a ROS 2 Lifecycle-based hardware abstraction layer to decouple autonomy from sensors (including mid-production LiDAR vendor swap), enabling mixed fleets and cutting bring-up/integration time dramatically. Also develops Nav2 plugins, MPPI tuning workflows, and scalable simulation/CI tooling (Gazebo, Docker, GitHub Actions) for high-throughput testing.”

“Software/product engineer who owns customer-facing internal platforms end-to-end, with deep experience building data pipeline health and data quality tooling (near-real-time alerting and ops dashboards). Strong in React/TypeScript + Python REST architectures and microservices with RabbitMQ, emphasizing reliability patterns (idempotency, DLQs, correlation IDs) and fast, safe iteration via feature flags, testing, and observability.”

“Graduate Research Assistant in Robotics at Arizona State University who built an end-to-end LLM-driven task execution framework enabling collaborative robots to convert high-level natural language instructions into safe, executable ROS actions. Implemented robust monitoring, failure detection, and automatic replanning, and addressed real-world issues like timestamp/frame-transform mismatches and heterogeneous robot interoperability using adapter nodes.”

“Ingeniero de Robótica y Automatización con 10 años de experiencia en instalaciones robóticas (principalmente sector aeronáutico) y proyectos de investigación en UK. En una gran empresa ferroviaria programó desde cero una instalación con 3 robots Staubli con tracks y desarrolló una plataforma de virtualización/simulación en ROS 2 (Gazebo/MoveIt 2), incluyendo integración industrial OPC UA/Profinet con PLC para 21 ejes y visualización en tiempo real.”

“Robotics software engineer/research associate with an MS in Robotics who built a ROS2-based, self-healing multi-robot fleet framework as a solo developer, combining LLM-driven decision-making (Gemini/ADK) with persistent fleet memory for cross-robot learning. Also has hands-on autonomy stack experience (Nav2, SLAM) and real-world robotics work including EKF sensor fusion for an excavator robot and closed-loop hydraulic control optimization.”

“Robotics software engineer with hands-on SLAM, ROS2, and distributed multi-robot systems experience. Improved MAST3R-SLAM loop-closure place recognition by changing the ASMK/ASMKS retrieval similarity metric (L2→L1) and validated on 9 TUM sequences, keeping near real-time performance despite a 25–30% retrieval cost increase. Also tuned MoveIt motion planning for a 6-DOF arm (12% higher maze completion rate) and built MQTT mesh communications for ESP32-based AMRs, using Gazebo+Docker and CI-style automation for reproducible testing and deployment.”

“Robotics/SLAM-focused engineer who worked on RT-Appearance mapping using NetVLAD, replacing traditional CV feature extraction with a deep learning approach to improve loop closure in repetitive green environments. Has hands-on ROS1/ROS2 experience (including bridging), point-cloud alignment with G-ICP for sensor-parameter matching, and Gazebo+Docker simulation testing for motion planning/perception.”

“Robotics/embedded candidate who built an end-of-studies restaurant waiter robot using Raspberry Pi 5 + ROS 2 Jazzy with Arduino Mega motor control and an ESP32-based table call button system. Implemented sensor nodes (LiDAR/ultrasonic/IMU), SLAM mapping with slam_toolbox, and navigation with Nav2; overcame major odometry calibration issues using EKF filtering and validated behavior in Gazebo before deploying to the real robot.”