Director of Engineering, Perception & Spatial AI

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. 

The role

In this role, 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.

• Own, 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.
• Remain deeply hands-on in architecture reviews, model design, technical investigations, debugging critical issues, and evaluation of new approaches. This role requires active technical contribution, not only leadership.
• Build and scale a high-performing perception engineering team from the ground up, hiring key talent, developing technical leaders, and establishing engineering standards, development processes, and a strong culture of technical excellence.

Must-Have Experience

• 10+ years of experience in ML, AI, computer vision, robotics, autonomous driving, spatial AI, or related fields including 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 systems and modern computer vision architectures, including object detection, segmentation, tracking, lane understanding, scene understanding, road infrastructure perception, and spatial AI concepts.
• Experience with deep learning frameworks, preferably PyTorch.
• Experience deploying AI/perception systems on embedded, automotive, robotics, or other hardware-constrained platforms.
• Hands-on experience deploying and optimizing AI/perception models for production, including latency, memory, throughput, quantization, compression, and deployment workflows such as TensorRT and ONNX.
• Experience with automotive-grade SoCs such as NVIDIA Orin, Qualcomm Snapdragon Ride, TI TDA4, or similar platforms.
• Experience building and scaling high-performing engineering teams, including hiring key talent, mentoring senior engineers, and establishing technical direction.
• Demonstrated ability to remain hands-on in architecture reviews, technical investigations, model development, and deployment decisions while leading a team.
• Strong communication skills, with the ability to explain technical trade-offs to both technical and non-technical stakeholders.
• Ability to work effectively across research, engineering, product, platform, and customer-facing teams.

• 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 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 structures.

• Experience with synthetic data, simulation pipelines, or sim-to-real validation.

• Experience with large-scale driving or robotics datasets such as nuScenes, Waymo Open Dataset, KITTI, Argoverse, or similar.

• Exposure to automotive safety standards such as ISO 26262 or SOTIF.

• Publications or strong research contributions in computer vision, perception, robotics, or machine learning.

• Experience in high-growth, scale-up, or fast-moving product environments.

HERE is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, age, gender identity, sexual orientation, marital status, parental status, religion, sex, national origin, disability, veteran status, and other legally protected characteristics.

As part of HERE Technologies employment process, candidates will be required to successfully complete a pre-employment screening process. This offer and any related claims are subject to the successful completion of a pre-employment screening. This will involve employment, education, and criminal verification if applicable.

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