Machine Learning Engineer

Staff Machine Learning Engineer – Scene Generation / Neural RenderingAbout the OpportunityWe’re partnering with a leading autonomous vehicle technology company to hire a Staff Machine Learning Engineer for its Scene Generation team.This is a highly technical leadership role focused on building the next generation of neural rendering and generative modeling technology for autonomous vehicle simulation. The team develops realistic, scalable sensor simulations from real-world data across camera, LiDAR, and radar modalities, helping create novel scenarios used to train and validate autonomous driving systems.This person will operate as a technical lead, combining hands-on development with technical direction, architecture ownership, mentorship, and the application of cutting-edge research to production systems.Location: Remote – US or CanadaCompensation: $249K–$299K base, with potential flexibility for exceptional candidatesWhat You’ll DoServe as a technical lead for a team focused on scene generation, neural rendering, and generative modeling.Set technical direction and make key architecture and design decisions.Develop state-of-the-art approaches using 3D Gaussian Splatting (3DGS), Neural Radiance Fields (NeRFs), and generative models.Build neural representations of complex real-world driving environments and generate novel scenes and viewpoints for simulation.Translate cutting-edge research into production-quality camera, LiDAR, and radar sensor simulation.Own neural rendering systems from research and prototyping through implementation, testing, scaling, and deployment.Work with large-scale, multimodal autonomous-driving datasets across multiple sensors and timestamps.Integrate ML and rendering systems into cloud-based training, simulation, and validation environments.Establish technical best practices and mentor engineers across the team.Collaborate with ML, robotics, simulation, perception, and infrastructure teams to bring new research into production.What We’re Looking ForDeep expertise in 3D Gaussian Splatting (3DGS), ideally demonstrated through industry work, research, publications, or significant open-source contributions.Strong experience with Neural Rendering, including technologies such as 3DGS and NeRFs.Background in Computer Vision, Computer Graphics, 3D Computer Vision, or 3D Reconstruction.Experience applying ML to robotics, autonomous vehicles, or other real-world 3D environments.Experience working with multimodal sensor data, ideally including cameras, LiDAR, and radar.Strong Python and PyTorch experience.Experience with generative modeling approaches such as Diffusion Models and/or Flow Matching.Track record of taking advanced ML or research concepts and turning them into reliable production systems.Staff-level technical leadership experience, including architecture ownership, technical decision-making, and mentoring other engineers.Bonus Points3D Gaussian Splatting experience specifically within robotics, autonomous driving, dynamic scenes, or sensor simulation.Publications at leading Computer Vision, AI, or Graphics conferences such as CVPR, ICCV, ECCV, SIGGRAPH, NeurIPS, ICLR, or ICML.CUDA or GPU programming experience for high-performance rendering.Experience with autonomous-driving perception or simulation systems.Experience with 3D labeling, sensor calibration, or neural sensor simulation pipelines.Experience with cloud ML infrastructure, MLOps, or distributed computing frameworks such as Ray.Previous experience operating as a Tech Lead or leading senior technical teams.The Ideal ProfileYou’re someone who sits at the intersection of 3D Gaussian Splatting, Neural Rendering, 3D Computer Vision, and Robotics.You’ve gone beyond experimenting with these technologies and have developed meaningful expertise applying them to complex real-world data. You’re equally comfortable reading the latest research, implementing new approaches in PyTorch, making system-level architecture decisions, and helping a team turn promising research into scalable production technology.This is an opportunity to take technical ownership of a highly specialized area of autonomous vehicle simulation and have a direct impact on how next-generation autonomous systems are trained and validated.