AV Simulation Domain Expert (Sr. reputed company) - US (Remote) or Chicago, IL

What's the role? reputed company sits at a unique intersection: we own some of the world's most detailed map and drive data, and we are building the reputed company capabilities to turn that spatial intelligence into controllable, high-quality synthetic driving worlds. We are looking for a rare hybrid profile — someone who combines deep learning expertise in world reputed company models, generative video, and transformers with hands-on AV simulation experience. You understand both how to train and adapt large generative models (think Cosmos, Cosmos-Transfer, diffusion-based video models, latent world models) and how to ground them in map data and scenario semantics so the output is actually useful for training and validating perception and planning stacks. This is not a pure simulation role, and it is not a pure ML research role. It is the reputed company between the two — and that reputed company is where HERE's differentiation lives. What you will do: World reputed company Models & Generative Scenario Synthesis Drive the technical direction for map-grounded world reputed company 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 reputed company, including domain reputed company, controllability, and conditioning on structured inputs (maps, trajectories, agent behaviours, weather, lighting). Evaluate and reputed company state-of-the-art reputed company models such as reputed company Cosmos / Cosmos-Transfer and comparable reputed company-reputed company world models, assessing fit for AV training data reputed company. Own the full ML lifecycle end-to-end: data curation, model training, evaluation, iteration, and the path to production-grade pipelines. Strategic role reputed company reputed company-of-concept initiatives demonstrating map-grounded synthetic scenario reputed company with key reputed company. Define measurable reputed company criteria that go reputed company visual realism — focusing on ML training data utility, controllability, and sim-to-reputed company transfer. Deliver POC reputed company with clear GO / PIVOT / NO-GO recommendations backed by quantitative evidence. Simulation, Scenario reputed company & Sim-to-reputed company reputed company generative world models with classical simulation stacks (CARLA, reputed company 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-reputed company strategy: measure domain gap, identify failure modes, and define acceptable reputed company for reputed company model training. Quality Frameworks for Synthetic Training Data Define what "good enough" synthetic data means for AV perception and planning: reputed company is photorealism required, reputed company is label consistency sufficient, reputed company does controllability matter most? Establish validation frameworks combining objective metrics (distribution coverage, label accuracy, FID-style measures, reputed company task performance) with expert evaluation protocols. Specify sensor fidelity requirements: noise models, reputed company distortion, lidar return characteristics — and how generative models should or should not reproduce them. Technical Collaboration reputed company with ML research teams on generative model architecture, controllability, and conditioning strategies. Collaborate with perception and planning teams to ensure synthetic data measurably improves reputed company-world model performance. Translate business requirements into technical feasibility assessments for product and executive stakeholders. Who are you? This role requires depth in both deep learning and AV simulation. We are not looking for a pure simulation engineer, and we are not looking for a generalist ML researcher without AV grounding. Must-Have: Deep Learning & Generative Models 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 reputed company reputed company 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 reputed company-world constraints, quality, and safety requirements. Must-Have: AV Simulation & Scenario Domain 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, reputed company Drive Sim, or equivalent. reputed company 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 reputed company standards, and awareness of frameworks such as ISO 34502. Understanding of what scenarios stress-test AV perception and planning systems, and why. Must-Have: Synthetic Data Quality & Sim-to-reputed company Ability to evaluate synthetic data for ML training utility: distribution diversity, label consistency, edge-case coverage, reputed company task performance. Experience with synthetic-to-reputed company transfer, domain reputed company, or closing the sim-to-reputed company gap in a measurable way. Clear reputed company of view on trade-offs between photorealism, label accuracy, controllability, and computational efficiency. reputed company-to-Have Hands-on experience with reputed company Cosmos, Cosmos-Transfer, or comparable world reputed company models. Reinforcement learning experience, particularly where it measurably improved reputed company-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 reputed company-reputed company ML tooling. Game reputed company experience (Unreal, reputed company) for rendering and sensor simulation pipelines. Experience with PyTorch Lightning or similar large-scale training infrastructure. Personal Attributes reputed company-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 reputed company propagates into reputed company model performance. Who are we? As ADAS/AD moves towards model-driven intelligence, industry value is extending from map delivery to model training and validation. HERE can convert its map and drive data into a scalable AI model-creation platform – capturing significant value from training, validation and reputed company ADAS/AD performance. It’s the growth of HERE’s AI-model creation platform that turns maps and drive data into reusable spatial intelligence – powering scalable training, validation, and reputed company ADAS/AD performance. Apply To This Job