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# Sr./Principal Engineer- Autonomous Vehicle Simulation Domain Expert

- **Company:** HERE
- **Location:** Amsterdam, Netherlands
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Training Data, Python (Programming Language), Machine Learning, Open Source Technology, Sensor Fusion, Pytorch, Transfer Learning, Deep Learning, Generative AI, Machine Learning Operations, Stable Diffusion, GPT
- **Published:** November 13, 2026
- **Apply:** https://www.adzuna.nl/details/5963479374

## About the Role

Deep Learning & Generative Models

 * Proven end-to-end model training experience with clear ownership across data, training, evaluation, and iteration.
 * Expertise in generative video, world models, or related generative AI - specifically diffusion models, latent video models, and/or transformer-based world models.
 * Experience with high-dimensional spatio-temporal data (video, multi-sensor fusion, driving data).
 * Strong Python and PyTorch fundamentals; track record of taking ML models from research to production under real-world constraints.

AV Simulation & Scenario Domain

 * 5+ years spanning AV simulation, AV perception/ML, or robotics simulation - with meaningful exposure to both simulation platforms and ML model development.
 * Hands-on experience with at least one major simulation platform: CARLA, NVIDIA Drive Sim, or equivalent.
 * Fluency in OpenDRIVE and OpenSCENARIO: able to author and generate scenario definitions programmatically.
 * Understanding of AV testing workflows, ASAM OpenX standards, ISO 34502, and what scenarios stress-test perception and planning systems.

Synthetic Data Quality & Sim-to-Real

 * Ability to evaluate synthetic data for distribution diversity, label consistency, edge-case coverage, and downstream task performance.
 * Experience with sim-to-real transfer, domain adaptation, or closing the domain gap measurably.
 * Clear point of view on trade-offs between photorealism, label accuracy, controllability, and computational efficiency.

## Description

* Drive technical direction for map-grounded world foundation models, conditioning generative video/world models on map data, drive data, scenario semantics, trajectories, agent behaviours, weather, and lighting.
 * Train, fine-tune, and adapt generative models (diffusion, latent video, transformer-based) for driving scenario generation with full ownership of the ML lifecycle: data curation, training, evaluation, and production pipelines.
 * Evaluate and extend state-of-the-art models such as NVIDIA Cosmos / Cosmos-Transfer and comparable open-source alternatives for AV training data generation.

Strategic Leadership

 * Lead POC initiatives for map-grounded synthetic scenario generation with key technology partners; define measurable success criteria beyond visual realism - focusing on ML training utility, controllability, and sim-to-real transfer.
 * Deliver GO / PIVOT / NO-GO recommendations backed by quantitative evidence.

Simulation & Sim-to-Real

 * Bridge generative world models with classical simulation stacks (CARLA, NVIDIA Drive Sim, AlpaSim) for physics-grounded scenarios.
 * Author and programmatically generate OpenSCENARIO / OpenDRIVE definitions for both classical and generative pipelines.
 * Drive sim-to-real strategy: measure domain gap, identify failure modes, and define acceptable thresholds for downstream model training.

Quality Frameworks for Synthetic Training Data

 * Define "good enough" synthetic data for AV perception and planning: when photorealism is required, when label consistency suffices, and when controllability matters most.
 * Establish validation frameworks combining objective metrics (distribution coverage, label accuracy, FID-style measures, downstream task performance) with expert evaluation protocols.
 * Specify sensor fidelity requirements: noise models, lens distortion, and lidar return characteristics.

Technical Collaboration

 * Interface with ML research, perception, and planning teams to ensure synthetic data measurably improves real-world model performance.
 * Translate business requirements into technical feasibility assessments for product and executive stakeholders.

NOTE: This role can be based in Berlin/Frankfurt/Munich/Amsterdam.

This role sits at the intersection of deep learning and AV simulation - and we need someone equally grounded in both. You have taken ML models from research into production, navigating real-world constraints, quality thresholds, and safety requirements along the way.