Director of Engineering, Perception & Spatial AI

About the role

As an Engineering Director - Perception & Spatial AI, you'll lead the architecture and delivery of perception models that run from cloud training through to deployment on automotive-grade hardware. A key part of the challenge is "design for deployment" from day one-building models that can meet strict latency and memory constraints on embedded/edge platforms, without compromising real-world performance. You will define the architecture, build a world-class team, and own the full journey from research through to deployment-ready models at scale.

Responsibilities

• Define and lead the end-to-end perception architecture -from cloud training to deployment-ready model variants for automotive-grade SoCs (e.g., Qualcomm Snapdragon Ride, NVIDIA Orin, or similar).
• Drive a deployment-first approach across architecture decisions, including quantization, latency targets, and memory constraints.
• Turn state-of-the-art perception research into reliable, scalable production pipelines (cloud + edge model variants).
• Guide BEV / multi-camera perception focused on road infrastructure (lanes, boundaries, signs, traffic lights, road surface attributes).
• Define evaluation and validation standards, including hardware-aware metrics (latency vs accuracy trade-offs, memory footprint, throughput on reference hardware).
• Partner closely with research, simulation, product, and customer/partner teams to ensure outputs are usable by downstream systems and meet real deployment needs.
• Stay hands-on by building and reviewing architectures, debugging critical issues, and prototyping new approaches.
• Mentor and grow a high-performing team (hiring, mentoring, setting technical direction, and establishing strong engineering practices).
• Who are you?
• Must-Have Experience
• 10+ years of experience in ML, AI, computer vision, robotics, autonomous driving, spatial AI, or related fields.
• 5+ years of hands-on experience with computer vision, perception, or scene understanding systems.
• Proven experience taking ML or computer vision models from research or prototype stage into production systems
• Strong understanding of perception tasks such as object detection, semantic segmentation, instance segmentation, lane detection, road boundaries, signs, traffic lights, or road surface attributes.
• Experience with deep learning frameworks, preferably PyTorch.
• Strong understanding of modern computer vision architectures, including multi-task learning and spatial scene understanding.
• Experience working with large-scale training pipelines, including distributed training, experiment tracking, and model versioning.
• Practical experience optimizing ML models for production, including latency, memory, throughput, and accuracy trade-offs.
• Familiarity with model deployment workflows such as ONNX export, TensorRT or similar inference optimization frameworks.
• Experience working with edge, embedded, automotive, robotics, mobile, or other hardware-constrained deployment environments.
• Strong technical leadership experience, including leading engineering or applied research teams, setting technical direction, mentoring engineers, and hiring talent.
• Ability to work across research, engineering, product, platform, and customer-facing teams.
• Strong communication skills, with the ability to explain technical trade-offs to both technical and non-technical stakeholders.
• Experience with large-scale training setups (multi-GPU/multi-node), and the ability to set practical MLOps standards (experiment tracking, model versioning, reproducibility).
• Curious and hands-on enough to stay close to emerging trends in perception, spatial AI, efficient models, and edge deployment.
• Good to Have
• Experience with BEV, multi-camera perception, 3D perception, lidar-camera fusion, or occupancy prediction.
• Experience with architectures such as BEVFormer, BEVFusion, or similar spatial perception models.
• Experience with automotive-grade SoCs such as NVIDIA Orin, Qualcomm Snapdragon Ride, TI TDA4, or similar platforms.
• Hands-on experience with quantization-aware training, post-training quantization, pruning, distillation, mixed-precision inference, or model compression.
• Experience benchmarking models on real hardware and working with latency, memory, and throughput constraints.
• Familiarity with QNN, TensorRT, graph optimization, operator compatibility, or hardware-specific compilation workflows.
• Experience with geospatial data, map priors, road topology, HD maps, or spatial data

Additional Information

What's the role? As ADAS/AD moves towards model-driven intelligence, industry value is extending from map delivery to model training and validation. HERE can convert its map and drive data into a scalable AI model-creation platform - capturing significant value from training, validation and next generation ADAS/AD performance. It's the growth of HERE's AI-model creation platform that turns maps and drive data into reusable spatial intelligence - powering scalable training, validation, and next generation ADAS/AD performance.

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