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HERE Technologies

The world's leading location platform company

AV Simulation Domain Expert – Senior Principal

PrincipalGeneralFull TimeRemoteSeniorTeam 5,001-10,000H1B No SponsorCompany SiteLinkedIn

Location

Illinois

Posted

10 days ago

Salary

0

Seniority

Senior

Bachelor Degree5 yrs expEnglishPythonPyTorch

Job Description

AV Simulation Domain Expert – Senior Principal

HERE Technologies

• 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.

Job 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

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