Robotics Software Engineer

About the Role

Roles & Responsibilities

• Architect, build, and deploy production-grade software stacks for Autonomous Mobile Robots (AMRs), covering autonomy, localization, motion control, and fleet integration.
• Design, build, and maintain efficient, modular, and reusable C++ and Python code adhering to strict safety and performance constraints.
• Implement, tune, and benchmark SLAM algorithms, dynamic obstacle avoidance, and global/local trajectory planning for real-world AMR deployments in dynamic environments.
• Integrate, calibrate, and filter data from diverse sensors (LiDARs, Depth Cameras, IMUs, Wheel Encoders) using robust sensor fusion techniques.
• Develop and debug robot behavior logic using Behavior Trees (Nav2 BT) and Finite State Machines (FSMs) for complex industrial workflows and docking routines.
• Architect low-latency, resilient inter-process and edge-to-cloud communication pipelines (DDS, MQTT, TCP/IP, WebSockets) connecting AMRs to Fleet Management Systems (FMS).
• Build simulation environments, mock pipelines, and automated test benches in Gazebo/Webots to validate software releases before on-robot deployment.
• Profile runtime bottlenecks, analyze memory/CPU footprints on edge computing hardware (e.g., NVIDIA Jetson, industrial x86/ARM IPCs), and devise targeted optimizations.
• Identify edge-case failures on hardware, triage field issues, and lead root-cause analysis (RCA) to drive software reliability.

Requirements

• Core Engineering: 2–5 years of hands-on experience in robot software engineering, specifically targeting mobile robotics or AMRs.
• Languages & Tooling: Strong proficiency in modern C++ (C++14/17/20) and Python; deep understanding of OOP design, memory management, data structures, and algorithms.
• Robotics Ecosystem: Hands-on expertise with ROS / ROS 2 (rclcpp, rclpy, composition, lifecycle nodes) and navigation frameworks (Nav2).
• Navigation & Autonomy: Strong practical working knowledge of:
• • SLAM & Localization: Cartographer, RTAB-Map, Nav2 AMCL, GraphSLAM, and EKF/UKF sensor fusion.
  • Planning & Control: A*, Dijkstra, Hybrid A*, DWA, TEB Local Planner, MPPI, and kinematic motion models (differential drive, omnidirectional, or Ackermann).
• Sensors & Hardware Interfaces: Proven track record interfacing and calibrating 2D/3D LiDARs, depth cameras (RealSense, Stereo), IMUs, and motor controller feedback over CAN, Serial, or Ethernet.
• Communication & Protocols: Strong understanding of DDS (CycloneDDS, FastDDS), MQTT, ZeroMQ, or WebSockets, balancing latency, throughput, and failover requirements.
• Environment & Build Tools: Fluent in Linux (Ubuntu), CMake, Bash scripting, Git, and hardware-in-the-loop (HIL) debugging.
• Software Quality: Solid experience with automated testing (GTest, PyTest), static analysis tools, and CI/CD pipelines.

Good to Have

• Hands-on experience integrating AMRs with Fleet Management Systems (e.g., Open-RMF, VDA 5050 standard) or warehouse execution systems (WES/WMS).
• Practical experience designing autonomous precision docking routines (using ArUco markers, reflectors, or visual servoing).
• Knowledge of Perception, Computer Vision (OpenCV), and Deep Learning edge inference (TensorRT, ONNX) for semantic obstacle detection.
• Familiarity with functional safety standards for industrial mobile robots (e.g., ISO 3691-4)
• Experience with containerized deployments using Docker for edge robotics.

Candidate Profile & Qualifications

• Education: B.E. / B.Tech / M.Tech in Computer Science, Robotics, Electronics, Mechatronics, Mechanical, or allied engineering domains.
• Mindset: Proven track record of thriving in fast-paced, high-ownership engineering environments.
• Problem Solving: Structured approach to root-cause debugging on real physical robots under constrained operational timelines.