Sr. Engineer II - Robotics
Location: Bangalore, India
Department: AIR
Experience: 4-6
Skills: ros2, Programming Language - Python, Robotics - Computer vision / Perception
- Build and ship production perception pipelines deployed on physical robotic systems.
- Develop ROS/ROS2 nodes and distributed systems for real-time perception (publish/subscribe, services, actions, TF).
- Integrate and fuse multi-sensor data (e.g., camera, LiDAR, radar, IMU, GPS) into a reliable environmental model.
- Own perception end-to-end: algorithm choice → implementation → optimization →deployment → field performance.
- Debug and harden systems where sensor data is noisy, incomplete, and imperfect.
- Partner with autonomy, hardware, and field teams to deploy quickly and iterate based on real-world results.
- Improve runtime performance (latency/throughput), reliability, logging/telemetry, and maintainability.
- 4+ years experience building real-world software, with meaningful time spent on robotics perception.
- ROS/ROS2 is required (professional experience building, deploying, and debugging ROS-based systems).
- Prior robotics application experience is required (robots, autonomous vehicles, drones, industrial automation, etc.).
- Strong programming skills in Python.
- Proven record shipping perception and/or sensor fusion capabilities into production or real deployments.
- Solid understanding of real-time constraints, sensor timing/synchronization, and system-level debugging.
- Comfortable making pragmatic engineering tradeoffs to meet performance and deployment goals.
- Able to work on-site
- Experience with sensor fusion, mapping, object detection/tracking, or multi-object tracking.
- Familiarity with common robotics/perception tooling (e.g., tf2, rosbag, rviz, image_transport, calibration pipelines).
- Experience deploying to edge compute (e.g., NVIDIA Jetson) and optimizing runtime performance.
- Field deployment experience: diagnosing issues from logs, telemetry, and on-robot behavior in messy environments.
- Startup / rapid iteration background.
- Reliable perception outputs across varied conditions, validated in real deployments (not just offline metrics).
- ROS/ROS2 perception stack that’s observable, debuggable, and maintainable (good logging, replay, tooling).
- Fast iteration loop from field feedback to shipped improvements.
- Python engineering and system design for robotics software
- ROS/ROS2 architecture and debugging (TF, timing, message flow, bags)
- Perception fundamentals + pragmatic production decisions
- Real-world deployment stories: failures, fixes, and what you learned
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