AV Simulation Domain Expert – Senior Principal

🕒 July 2

🌽 Illinois – Remote

🌽 Illinois – Remote

⏰ Full Time

🟠 Senior

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HERE Technologies

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

💼 Consulting

📦 Logistics

🚀 Aerospace

Consulting • Logistics • Aerospace

HERE Technologies is a leading provider of mapping and location data services, specializing in advanced location solutions for various industries, including automotive. Their products range from dynamic map content and routing services to tools for automated driving and fleet management. With a focus on precision and innovation, HERE Technologies empowers businesses with high-quality geographical data to enhance navigation and develop next-generation, software-defined vehicle systems.

📋 Description

• Drive the technical direction for map-grounded world foundation models: how we condition generative video and world models using map data, drive data, and scenario semantics. • Train, fine-tune, and adapt generative models (diffusion, latent video, transformer-based world models) for driving scenario generation, including domain adaptation, controllability, and conditioning on structured inputs (maps, trajectories, agent behaviours, weather, lighting). • Evaluate and extend state-of-the-art foundation models such as NVIDIA Cosmos / Cosmos-Transfer and comparable open-source world models, assessing fit for AV training data generation. • Own the full ML lifecycle end-to-end: data curation, model training, evaluation, iteration, and the path to production-grade pipelines. • Lead proof-of-concept initiatives demonstrating map-grounded synthetic scenario generation with key technology partners. • Define measurable success criteria that go beyond visual realism — focusing on ML training data utility, controllability, and sim-to-real transfer. • Deliver POC outcomes with clear GO / PIVOT / NO-GO recommendations backed by quantitative evidence. • Bridge generative world models with classical simulation stacks (CARLA, NVIDIA Drive Sim, AlpaSim) where structured, physics-grounded scenarios are needed. • Author and programmatically generate OpenSCENARIO / OpenDRIVE definitions that feed both classical simulators and generative pipelines. • Drive sim-to-real strategy: measure domain gap, identify failure modes, and define acceptable thresholds for downstream model training. • Define what 'good enough' synthetic data means for AV perception and planning: when is photorealism required, when is label consistency sufficient, when does controllability matter 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, lidar return characteristics — and how generative models should or should not reproduce them. • Interface with ML research teams on generative model architecture, controllability, and conditioning strategies. • Collaborate with 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.

🎯 Requirements

• 5+ years combined experience spanning AV simulation, perception/ML for AVs, 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 with OpenDRIVE and OpenSCENARIO: can author and generate scenario definitions programmatically and understands map format specifications. • Understanding of AV testing workflows: scenario-based validation, ASAM OpenX standards, and awareness of frameworks such as ISO 34502. • Understanding of what scenarios stress-test AV perception and planning systems, and why. • Proven experience training deep learning models end-to-end, with clear ownership across data, training, evaluation, and iteration. • Expertise in generative video, world models, or related generative AI research/engineering. • Deep working knowledge of diffusion models, latent video models, and/or transformer-based world models. • Experience with high-dimensional temporal or spatio-temporal data (video, multi-sensor fusion, driving data). • Strong Python and PyTorch engineering fundamentals; comfortable building research-grade tooling that can scale toward production. • Demonstrated ability to take ML models from research into production, navigating real-world constraints, quality, and safety requirements.

🏖️ Benefits

• Not specified