AV Simulation Domain Expert (Sr. Principal) - US (Remote) or Chicago IL

• 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.

• 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.

• 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.

• Ability to evaluate synthetic data for ML training utility: distribution diversity label consistency edge-case coverage downstream task performance.

• Experience with synthetic-to-real transfer domain adaptation or closing the sim-to-real gap in a measurable way.

• Clear point of view on trade-offs between photorealism label accuracy controllability and computational efficiency.

• Hands-on experience with NVIDIA Cosmos Cosmos-Transfer or comparable world foundation models.

• Reinforcement learning experience particularly where it measurably improved real-world performance.

• Experience with end-to-end driving models.

• Automotive OEM or other safety-critical ML deployment experience (ISO 26262 SOTIF awareness).

• Strong publication record in generative models world models or AV ML; or significant contributions to open-source ML tooling.

• Game engine experience (Unreal Unity) for rendering and sensor simulation pipelines.

• Experience with PyTorch Lightning or similar large-scale training infrastructure.

• Bridge-builder: fluent translator between ML researchers simulation engineers AV domain experts and product managers.

• Hands-on: you validate assumptions by training models and running simulations not by writing specs.

• Quality-obsessed: you define objective standards where others see subjective judgments.

• Pragmatic: you balance "state-of-the-art realism" against "measurably useful for training."

• Systems thinker: you understand how every choice in data generation propagates into downstream model performance.

What We Do

HERE Technologies is a location data and technology company that created the first digital map over 35 years ago. Today we are the world's leading location platform company with a global footprint across 52 countries. Although our strongest presence is in the automotive industry we also work with leading companies across a wide range of industries including transport and logistics mobility manufacturing and retail and the public sector.

Why Work With Us

At HERE we're always excited about discovering people who share our passion for building innovative solutions that make the world easier to navigate. We believe our success is powered by our team's diversity creativity and collaboration and we're always looking for opportunities to grow it further.

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