Lead Software Engineer, Perception (Robotics)

About the role

Hiring employer: Grab

As a Senior Perception & Prediction Engineer, you will build and ship systems that turn raw multi-sensor data into a reliable, real-time and predictive representation of the world.

Responsibilities

• You will develop and improve multi-object tracking — data association, Bayesian state estimation (Kalman / EKF / UKF and motion models), track lifecycle, and ID stability through detector gaps, occlusions and crowded scenes — and evolve it toward learning-based tracking, including learned association, joint detection-and-tracking and query / transformer-based trackers.
• You will build and harden generic / open-set world understanding — class-agnostic obstacle detection, occupancy grids / occupancy networks / BEV occupancy, clustering and learned generic-object branches — so the robot safely reacts to rare or unknown objects.
• You will build motion prediction for pedestrians, cyclists, vehicles and other agents, including multi-modal trajectory forecasting, interaction-aware prediction, occupancy flow, uncertainty estimation and well-defined interfaces into behaviour and planning.
• You will develop and evaluate end-to-end temporal perception-prediction approaches, such as joint detection-tracking-forecasting, streaming BEV representations, agent / trajectory queries and learned world models, while maintaining strong modular baselines and production fallbacks.
• You will explore VLM / VLA and embodied foundation models for open-vocabulary perception, semantic scene reasoning, task-conditioned understanding, long-tail discovery, auto-labeling and teacher-student supervision. You will test grounding, hallucination, temporal consistency, latency and robustness, then distil, quantise or adapt useful capabilities for on-robot deployment rather than stopping at demos.
• You will strengthen multi-sensor and temporal integration across camera, LiDAR, radar and IMU, ensuring geometry, timing, identities, predictions and semantic context remain consistent over time.
• You will define quality gates across tracking (MOTA / MOTP-style, ID-switch, fragmentation), prediction (ADE / FDE, miss rate, NLL / calibration), generic-object / occupancy / occupancy-flow recall, closed-loop safety outcomes and embedded latency budgets, and drive down regressions with fleet-log root-cause analysis.
• You will partner with Detection, Prediction, Planning, Data & Infra, and Integration teams to move classical, learning-based and foundation-model capabilities from research into reproducible training, replay, simulation, NVIDIA Orin / TensorRT deployment and production-like robot testing.

How to apply: Apply here: https://www.grab.careers/en/jobs/744000154574849/lead-software-engineer-perception-robotics/ Apply via the company posting: https://www.grab.careers/en/jobs/744000154574849/lead-software-engineer-perception-robotics/

Source: company careers page

Company website: https://www.grab.com/

Requirements

- At least 3 years hands-on experience shipping or operating perception and / or prediction systems for robotics, AV or ADAS, with strong autonomous-driving experience and a record of improving real-world performance. - Rich experience across classical / geometric and learning-based approaches, with sound judgement on when to use modular methods, end-to-end models or hybrid designs, and how to retain interpretable safety fallbacks. - Proven depth in multi-object tracking — data association, Bayesian filtering, track lifecycle, multi-sensor / temporal association, ID stability and low-latency evaluation; experience with learned or query-based tracking is valuable. - Hands-on experience with generic / open-set perception — class-agnostic obstacle detection, occupancy representations, LiDAR clustering, anomaly / unknown-object handling or fusion with learned detections. - Hands-on depth in at least one modern temporal area: motion forecasting, occupancy flow, streaming / temporal BEV, learned world models, or joint detection-tracking-prediction. You should understand multi-modal uncertainty and the interface between perception, prediction and planning. - Fundamentals in geometry and multi-sensor systems, including practical calibration know-how and the ability to diagnose alignment or time-synchronisation issues that impact model quality. - Proficiency in C++ and Python, with the ability to write production-quality code, build training / evaluation tools and reason about real-time embedded performance. - Experience navigating noisy production-like environments through reproducible experiments, clear metrics, log-driven root-cause analysis, simulation / replay and validation-minded execution. - Self-motivated and dependable, with genuine enthusiasm for autonomous driving, robotics and embodied AI, and the judgement to separate robust progress from research hype. - Demonstrated proficiency in leveraging AI tools and systems to accelerate engineering and research without compromising quality, security or reproducibility. Nice to have: - End-to-end perception and prediction: joint detection-tracking-forecasting, occupancy / occupancy-flow prediction, streaming BEV, agent / trajectory queries or world models. - Motion prediction for autonomous systems: multi-modal trajectories, interaction-aware forecasting, uncertainty calibration, planning interfaces, closed-loop simulation or behaviour evaluation. - Experience with VLM / VLA / embodied foundation models, including vision-language grounding, open-vocabulary perception, task or instruction conditioning, semantic scene reasoning, long-tail mining or action-relevant representations. - Foundation-model adaptation and deployment: supervised fine-tuning / LoRA, preference or imitation data, distillation, quantisation, teacher-student pipelines, edge inference and systematic evaluation of hallucination and grounding. - 3D / BEV detection and sensor fusion (for example BEVFusion / LSS), temporal models, 4D data and multi-camera / LiDAR / radar integration. - Experience with common robotics / perception libraries and platforms such as OpenCV, PCL, Ceres, ROS 2, NVIDIA Orin, TensorRT or DeepStream, plus strong visualisation and debugging practices. - Familiarity with the model lifecycle: dataset curation, auto-labeling, temporal annotation, active learning, regression detection, inference optimisation and fleet-data feedback loops.