Senior Machine Learning Engineer, Applied Intuition

• 5+ years of experience developing and shipping ML or computer vision systems
• A minimum of a Bachelor's degree in computer science, physics, robotics, or equivalent
• Strong hands-on experience with diffusion models and video generation (e.g., latent and video diffusion models, diffusion transformers)
• A solid foundation in generative modeling and deep learning, including training and fine-tuning large models
• Proficiency in Python and PyTorch
• A proven ability to turn research ideas into robust, production-quality software
• Strong problem-solving skills and comfort with ambiguity
• If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway

Desirable

• We are looking for senior machine learning engineers to advance the generative modeling technology behind Neural Simulation, our state-of-the-art product for turning real-world driving data into high-fidelity, photorealistic simulation environments
• As part of this team, you will push the boundaries of what the product can do with diffusion and video generation models, creating realistic, controllable sensor data, augmenting real-world logs with new scenarios, and making the product more useful for customers who rely on it to train and validate their autonomy systems. Your work will directly shape how the largest OEMs in the world develop the next generation of data-driven autonomous vehicles
• This role is ideal for engineers who thrive at the intersection of generative modeling, computer vision, and machine learning, and who are excited to take a state-of-the-art product further by bringing the latest research into production and solving the hardest simulation gaps in Physical AI
• Develop and advance diffusion and video generation models that power our Neural Simulation product, bringing the latest research into production to improve realism, controllability, and scalability
• Push the limits of generative simulation for driving scenes, including:
• Controllable generation conditioned on scene layout, camera pose, actors, and trajectories
• Temporally consistent, multi-camera video generation
• Augmenting real-world logs with new scenarios, actors, and conditions such as weather and lighting
• Combine generative models with our neural reconstruction pipeline to improve fidelity and coverage of simulated scenes
• Scale training and inference of large generative models for production workloads
• Define and build evaluation metrics, benchmarks, and validation workflows that measure realism, temporal consistency, controllability, and sim-to-real gap
• Work closely with customers to understand their pain points and implement technical solutions in the Neural Simulation product
• Collaborate closely with Infra, Autonomy, Research and other product teams to deliver end-to-end solutions
• Take ownership of critical technical components and influence architecture and product decisions