说明

Grab 正在招聘一名高级感知与预测工程师，负责构建并交付用于机器人配送的实时感知和预测系统。该职位将开发多目标跟踪、通用开放集世界理解、运动预测、端到端时序感知-预测模型，以及 VLM/VLA 或具身基础模型能力，同时维护模块化基线、安全回退方案和生产级评估。工程师将参与多传感器集成、嵌入式部署、仿真、测试和车队数据验证，向高级首席感知与预测工程师汇报，并在 Grab 办公室现场办公。

所需技能

多目标跟踪
贝叶斯滤波
运动预测
C++
Python
传感器融合
VLM/VLA

附加信息

Required Previous Experiences: At least 3 years of hands-on experience shipping or operating perception or prediction systems for robotics, autonomous vehicles, or ADAS, with strong autonomous-driving experience and a record of improving real-world performance.
Proven depth in multi-object tracking, including data association, Bayesian filtering, track lifecycle, multi-sensor and temporal association, ID stability, and low-latency evaluation.
Hands-on experience with generic or open-set perception, including class-agnostic obstacle detection, occupancy representations, LiDAR clustering, and anomaly or unknown-object handling.
Hands-on depth in at least one modern temporal area, including motion forecasting, occupancy flow, streaming or temporal BEV, learned world models, or joint detection-tracking-prediction.
Fundamentals in geometry and multi-sensor systems, including practical calibration and the ability to diagnose alignment or time-synchronisation issues.
Proficiency in C++ and Python, including production-quality code, training and evaluation tools, and real-time embedded performance.
Experience navigating noisy production-like environments through reproducible experiments, metrics, log-driven root-cause analysis, simulation, replay, and validation.
Preferred Qualifications: End-to-end perception and prediction, including joint detection-tracking-forecasting, occupancy or occupancy-flow prediction, streaming BEV, agent or trajectory queries, and world models.
Motion prediction for autonomous systems, including multi-modal trajectories, interaction-aware forecasting, uncertainty calibration, planning interfaces, and closed-loop simulation or behaviour evaluation.
Experience with VLM/VLA or embodied foundation models, including vision-language grounding, open-vocabulary perception, task or instruction conditioning, semantic scene reasoning, and long-tail mining.
Foundation-model adaptation and deployment, including supervised fine-tuning, LoRA, distillation, quantisation, teacher-student pipelines, edge inference, and hallucination or grounding evaluation.
3D or BEV detection and sensor fusion, temporal models, 4D data, and multi-camera, LiDAR, or radar integration.
Experience with robotics and 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, including dataset curation, auto-labeling, temporal annotation, active learning, regression detection, inference optimisation, and fleet-data feedback loops.
Translation Company Name: Grab

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高级软件工程师，感知（机器人） at Grab

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