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Applied Intuition is a $15B-valued AI company (founded 2017) building digital infrastructure for physical AI across automotive, defense, trucking, construction, mining, and agriculture. The company serves 18 of the top 20 global automakers and the US military.
You will join the generative modeling team advancing Neural Simulation, a state-of-the-art product that transforms real-world driving data into high-fidelity, photorealistic simulation environments. This role focuses on pushing the boundaries of diffusion and video generation models to create realistic, controllable sensor data and augment real-world logs with new scenarios. Your work directly impacts how the world's largest OEMs develop next-generation data-driven autonomous vehicles.
Key responsibilities:
- Develop and advance diffusion and video generation models for the Neural Simulation product, bringing cutting-edge research into production
- Build controllable generation conditioned on scene layout, camera pose, actors, and trajectories
- Create temporally consistent, multi-camera video generation
- Augment real-world logs with new scenarios, actors, weather, and lighting conditions
- Combine generative models with neural reconstruction pipelines to improve fidelity and scene coverage
- Scale training and inference of large generative models for production workloads
- Define evaluation metrics, benchmarks, and validation workflows measuring realism, temporal consistency, controllability, and sim-to-real gap
- Work closely with customers to understand pain points and implement technical solutions
- Collaborate with Infra, Autonomy, Research, and product teams on end-to-end solutions
- Own critical technical components and influence architecture and product decisions
The company operates in-office (5 days/week from Sunnyvale HQ), with flexibility for occasional remote work and schedule adjustments. Additional offices in Washington DC, San Diego, Ft. Walton Beach, Ann Arbor, London, Stuttgart, Munich, Stockholm, Bangalore, Seoul, and Tokyo.
REQUIREMENTS:
- 5+ years developing and shipping ML or computer vision systems
- Bachelor's degree minimum in computer science, physics, robotics, or equivalent
- Strong hands-on experience with diffusion models and video generation (latent diffusion, video diffusion, diffusion transformers)
- Solid foundation in generative modeling and deep learning, including training and fine-tuning large models
- Proficiency in Python and PyTorch
- Proven ability to turn research ideas into robust, production-quality software
- Strong problem-solving skills and comfort with ambiguity
NICE TO HAVE:
- Experience with learning-based 3D reconstruction (3D Gaussian Splatting, NeRFs, feed-forward Gaussian Splatting)
- Background in computer vision (SfM, SLAM, photogrammetry) and/or computer graphics (rendering, rasterization, ray tracing)
- Track record shipping ML products with defined evaluation metrics and benchmarks
- Experience in autonomous driving or robotics with multi-sensor data (camera, LiDAR, radar)
- Experience with 3D-aware or multi-view consistent generation, or world models
- Large-scale distributed training and inference optimization (distillation, efficient sampling)
- C++ and/or CUDA programming
- Peer-reviewed research at CVPR, ICCV/ECCV, NeurIPS, ICLR, ICML, or SIGGRAPH
- Master's degree or PhD in computer science, physics, robotics, or related fields
