Senior Robotics Software Engineer

Summary

Own end-to-end development of autonomous capabilities (perception →

localization → planning → control) with a strong emphasis on simulation-first

workflows. Lead ROS 2 architecture, sensor integration, Nav2 customizations, and

motion planning, delivering features that transfer from sim to real robots. Responsibilities

 Simulation-first delivery

o Build/maintain high-fidelity worlds, robots, and sensors in

Gazebo/Ignition and/or Isaac Sim; create reproducible SIL/HIL

pipelines and regression suites. o Develop domain-randomization and sensor-noise models to improve

sim-to-real transfer.  Sensors & Estimation

o Integrate and calibrate LiDAR, camera, depth, radar, IMU, GNSS;

manage extrinsics and time sync (PTP/NTP). o Implement and tune EKF/UKF (e.g., robot_localization) and map/pose

graph maintenance.  Navigation & Motion Planning

o Own Nav2 stack: behavior trees, costmaps, planners/controllers;

obstacle layers and recovery behaviors. o Implement global/local planners: sampling-based (RRT*, PRM) and

trajectory optimization (CHOMP, TrajOpt, MPC).  ROS 2 Architecture

o Design scalable node graphs, lifecycle nodes, parameters, and TF

trees; tune DDS QoS for reliability/latency. o Build robust launch systems, tooling, and observability (rosbag2, tracing, metrics).  Performance & Re

o Profiling (perf, valgrind, sanitizers), latency budgets, CPU pinning;

PREEMPT_RT (nice-to-have). o Establish test strategy: unit/integration/e2e in sim, automated

scenario testing, fault injection, and KPIs.  Collaboration & Leadership

o Write clear design docs/RFCs; mentor teammates; partner with

hardware for bring-up and with ML for perception models. Minimum Qualifications

 6–10+ years in robotics; 3+ years with ROS 2 (Foxy+).  Strong C++17/20 and Python; solid Linux/Ubuntu.  Deep hands-on with Gazebo/Ignition (or Isaac Sim) for building

robots/worlds, plugins, and sensor models.

 Proven work with sensor fusion (IMU/LiDAR/camera/GNSS) and calibration

pipelines.  Nav2 expertise (BT customization, costmaps, planners/controllers) and

SLAM (slam_toolbox/Cartographer).  Motion planning experience: at least one of sampling-based or trajectory

optimization, plus constraints/collision checking (FCL).  Fluency with colcon/ament, CMake, Git, CI; debugging with gdb and tracing

with ros2_tracing. Nice-to-Have (NVIDIA Ecosystem & Acceleration)

 Isaac Sim/Omniverse workflows, USD assets, synthetic data pipelines.  Deployments on Jetson (CUDA, TensorRT, nvblox, Isaac ROS GEMs), ONNX/TensorRT model packaging.  DDS vendor tuning (Fast DDS, Cyclone DDS); SROS2 (security).  Real-time (PREEMPT_RT), EtherCAT/CAN basics; micro-ROS/RTOS

exposure. Tooling Stack

 ROS 2 (rclcpp/rclpy, TF2, Nav2, slam_toolbox/Cartographer, RViz, rqt).  Sim: Gazebo/Ignition, (bonus: Isaac Sim).  Perception: OpenCV, PCL; (bonus: PyTorch/TensorRT for inference

wrappers).  Testing/Obs: rosbag2, pytest/rostest, scenario runners, metrics dashboards.

 Build/CI: CMake, colcon, GitHub Actions/Jenkins; Docker for reproducible

dev. What Success Looks Like (first 6–12 months)

 90%+ of nav regressions automated in sim; green within 20 min per PR.  Reliable Y% success on benchmark

routes/scenarios.  Robust sim-to-real transfer:

(tracking error, collision rate, time-to-goal).  Reduced field bugs via failure injection and scenario coverage. Interview Signals

 Practical: build a Nav2 BT plugin to handle dynamic obstacles; instrument

and reduce planning latency with QoS/threads.  Design: draw a ROS 2 graph for multi-sensor fusion + Nav2, include TF tree

and lifecycle, discuss failure modes.  Simulation: create a sensor model with noise/latency and show how you'd

validate it against real logs.  Depth probes: extrinsics/time-sync strategy; local vs global planner

tradeoffs; MPC vs sampling; ros2_tracing usage.

Short checklist against your original list (all covered)  Kinematics/Dynamics, Control, Planning/SLAM, Perception

 Simulation frameworks & physics engines (emphasis on Gazebo/Ignition;

Isaac Sim as plus)  AI/ML for perception (good to have)

 Linux, C++/Python, ROS 2 Core

 Navigation2, Sensor Fusion, Calibration

 DevEx & Reliability (CI, tracing, bags, KPIs) — explicitly added for senior

roles

Job Type: Full-time

Pay: ₹1,000, ₹2,500,000.00 per year

Work Location: In person