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About the Role

As ADAS/AD technology advances, the value shifts towards model-driven intelligence, including training and validation. HERE's AI-model creation platform transforms maps and drive data into reusable spatial intelligence for scalable training, validation, and next-generation ADAS/AD performance. This Principal Engineer role involves hands-on work with cutting-edge architectures, large-scale distributed training, model quantization and compression for embedded systems, and proprietary datasets, pushing perception AI from research to real-world deployment.

Responsibilities

Develop and train infrastructure perception models for lane detection, road boundaries, traffic signs/lights, and road surface attributes.
Build BEV-based scene representation models (e.g., BEVFormer, BEVDet, BEVFusion), run large-scale distributed training, integrate geospatial priors, and validate synthetic training data.
Own the model optimization pipeline for edge deployment, including ONNX export, TensorRT/QNN compilation, operator compatibility checks, graph optimization, quantization, compression, and accuracy recovery.
Profile and benchmark models on embedded hardware, identifying latency, throughput, memory, and bottleneck issues.
Build automated regression pipelines to validate models against release targets.
Operationalize novel architectures by translating state-of-the-art research into reproducible, scalable training pipelines.
Design rigorous experiments, including A/B tests and ablation studies.
Evaluate cloud accuracy, deployment readiness, distribution coverage, temporal consistency, spatial coherence, and performance trade-offs.
Partner with research, simulation, and technical leadership to ensure perception outputs meet downstream requirements.
Support key architecture decisions around quantization and precision.
Design experiments to measure real-world gains from synthetic training.
Maintain high standards for code quality, documentation, extensibility, and mentor junior engineers.

Requirements

Self-directed and pragmatic engineer capable of owning complex problems end-to-end.
Ability to think across the full ML lifecycle from large-scale model training to SoC deployment.
Effective collaboration skills while maintaining high standards for code quality, documentation, and knowledge sharing.
5–7 years in ML/AI with 3+ years in computer vision, perception or deep learning systems.
Proven experience shipping production perception models into real-world systems or large-scale data pipelines.
Hands-on edge deployment experience — optimized and deployed models on embedded hardware or automotive-grade SoCs.
Strong PyTorch expertise, including building custom architectures, loss functions, and training loops.
Experience with distributed training using DDP/FSDP, mixed precision, and gradient checkpointing.
Experience with large-scale distributed training beyond fine-tuning pre-trained models.

Skills

BEV or multi-camera transformer architectures (BEVFormer, BEVDet, BEVFusion, occupancy networks)
Multi-task learning
Scalable training infrastructure
Data loading optimization
Augmentation pipelines
Experiment tracking tools (W&B, MLflow)
Structured scene understanding (road topology, lane geometry, road surface attributes)
Semantic/instance segmentation of infrastructure elements
BEV-space, occupancy-grid, or map-aligned representations
ONNX
TensorRT/QNN
Quantization workflows (QAT, PTQ, mixed-precision inference)
Per-layer sensitivity analysis
Accuracy recovery
Model compression techniques
Automotive SoC platforms (Qualcomm Snapdragon Ride, NVIDIA Orin, TI TDA4, or comparable)
Profiling latency on real hardware
Identifying bottlenecks
Analyzing memory bandwidth
Optimizing inference throughput
Evaluating models beyond single metrics (data coverage, failure modes, edge cases)
Validating deployment readiness (accuracy-latency trade-offs, regression testing)
Reproducing research papers
Adapting architectures
Integrating architectures into scalable, production-ready training loops

Location

Berlin

Work Type

Hybrid
Full-time

Experience Level

Principal Engineer
5-7 years in ML/AI
3+ years in computer vision, perception or deep learning systems

Salary/Compensations

Competitive salary plus bonus

Benefits

Great work-life balance
30 paid vacation days
On-site Gym and Sauna (For Berlin location)
Yoga Room (For Berlin location)
Flexible working hours
BVG Ticket (For Berlin location)
German language course (For Germany-based employees)
Employee wellness programs and life-coaching sessions
Brown bag talks, team events, BBQ on the rooftop

About the Company

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.

Equal Opportunity

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.