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HERE Technologies

PaaS for location-based solutions

Senior/Principal Engineer-Autonomous Vehicle Simulation Domain Expert

Applied Machine Learning +1Updated on Nov 12
Full-time

About the Job

Requirements

  • Proven end-to-end model training experience, with 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, including video, multi-sensor fusion, or driving data.
  • Strong Python and PyTorch fundamentals, and a track record of taking machine-learning models from research to production under real-world constraints.
  • At least 5 years of experience spanning autonomous vehicle simulation, autonomous vehicle perception or machine learning, or robotics simulation, with meaningful exposure to both simulation platforms and machine-learning model development.
  • Hands-on experience with at least one major simulation platform: CARLA, NVIDIA Drive Sim, or an equivalent platform.
  • Fluency in OpenDRIVE and OpenSCENARIO, including the ability to author and generate scenario definitions programmatically.
  • Understanding of autonomous vehicle testing workflows, ASAM OpenX standards, ISO 34502, and scenarios that stress-test perception and planning systems.
  • 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 measurably closing the domain gap.
  • A clear point of view on trade-offs between photorealism, label accuracy, controllability, and computational efficiency.

Responsibilities

  • Drive technical direction for map-grounded world foundation models by conditioning generative video and world models on map data, drive data, scenario semantics, trajectories, agent behaviours, weather, and lighting.
  • Train, fine-tune, and adapt generative models—including diffusion, latent video, and transformer-based models—for driving scenario generation, owning the machine-learning lifecycle across data curation, training, evaluation, and production pipelines.
  • Evaluate and extend state-of-the-art models such as NVIDIA Cosmos and Cosmos-Transfer, along with comparable open-source alternatives, for autonomous vehicle training data generation.
  • Lead proof-of-concept initiatives for map-grounded synthetic scenario generation with key technology partners, defining measurable success criteria focused on machine-learning training utility, controllability, and sim-to-real transfer in addition to visual realism.
  • Deliver go, pivot, or no-go recommendations supported by quantitative evidence.
  • Bridge generative world models with classical simulation stacks, including CARLA, NVIDIA Drive Sim, and AlpaSim, to create physics-grounded scenarios.
  • Author and programmatically generate OpenSCENARIO and OpenDRIVE definitions for classical and generative pipelines.
  • Drive sim-to-real strategy by measuring the domain gap, identifying failure modes, and defining acceptable thresholds for downstream model training.
  • Define quality criteria for synthetic autonomous-vehicle perception and planning data, including when photorealism, label consistency, or controllability is most important.
  • Establish validation frameworks that combine objective metrics—such as distribution coverage, label accuracy, FID-style measures, and downstream task performance—with expert evaluation protocols.
  • Specify sensor fidelity requirements, including noise models, lens distortion, and lidar return characteristics.
  • Interface with machine-learning 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.

About the Company

HERE Technologies

PaaS for location-based solutions

HERE Technologies provides a location-based Platform-as-a-Service (PaaS) for automotive, transportation, logistics, and enterprise customers. Its offerings are scalable, customizable location services that developers can integrate into apps and systems via APIs and SDKs, enabling features like maps, routing, geocoding, and traffic insights. The company differentiates itself through global reach, industry-focused solutions, and a flexible subscription/licensing model that supports custom deployments and integrations. Its goal is to help businesses build location-enabled applications and optimize operations by delivering reliable, adaptable location data and services at scale.

Company size
5,001-10,000
Company stage
IPO
Headquarters
Eindhoven, Netherlands
Founded
1985

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