Distinguished Engineer, Perception, Embedded AI & SoC

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.

• What You’ll Do
• Own, define and lead the end‑to‑end perception architecture—from cloud training to deployment‑ready model variants for automotive‑grade So Cs (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.

• Help 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.
• Partner closely with customers to understand real‑world challenges and translate them into scalable solutions.

Who are you?

• 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 Py Torch.
• 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 Tensor RT and ONNX.
• Experience with automotive‑grade So Cs such as NVIDIA Orin, Qualcomm Snapdragon Ride, TI TDA4, or similar platforms.
• 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 building and scaling high‑performing engineering teams is an added advantage

While perception experience is valuable, this role is also focused on AI deployment, embedded architectures, So C platforms, inference optimization and production‑grade edge AI systems rather than development of perception algorithms alone.

• 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 benchmarking models on real hardware and working with latency, memory, and throughput constraints.

Familiarity with QNN, Tensor RT, 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 nu Scenes, 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.

This role can be based in Berlin, Frankfurt, Munich or Amsterdam.

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.

Who are we?

HERE Technologies is a location data and technology platform company.

We empower our customers to achieve better outcomes – from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely.

At HERE we take it upon ourselves to be the change we wish to see.

We create solutions that fuel innovation, provide opportunity and foster inclusion to improve people’s lives.

If you are inspired by an open world and driven to create positive change, join us.

Learn more about us on our You Tube Channel.

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