Senior Machine Learning Engineer (Generative Models)

Join our team as a Senior Machine Learning Engineer specializing in generative models. You will work on advancing the technology behind our Neural Simulation product, which transforms real-world driving data into high-fidelity, photorealistic simulation environments. Your work will directly impact the development of data-driven autonomous vehicles by pushing the boundaries of generative modeling, computer vision, and machine learning.

Responsibilities

• Advancing the generative modeling technology behind Neural Simulation, focusing on diffusion and video generation models.
• Pushing the limits of generative simulation for driving scenes, including controllable generation and temporally consistent video generation.
• Collaborating closely with customers to understand their pain points and implement technical solutions in the Neural Simulation product.
• A proven ability to turn research ideas into robust, production-quality software

Requirements

• 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 problem-solving skills and comfort with ambiguity
• A solid foundation in generative modeling and deep learning, including training and fine-tuning large models

Proficiency in Python and PyTorch

• Strong hands-on experience with diffusion models and video generation (e.g., latent and video diffusion models, diffusion transformers)
• 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
• A background in computer vision (e.g., SfM, SLAM, photogrammetry) and/or computer graphics (e.g., rendering, rasterization, ray tracing)
• A track record of shipping ML products with clearly defined evaluation metrics and benchmarks
• Programming experience in C++ and/or CUDA
• A Master's degree or PhD in computer science, physics, robotics, or related fields

Preferred

• Experience with learning-based 3D reconstruction, such as 3D Gaussian Splatting, NeRFs, or feed-forward Gaussian Splatting
• Experience in autonomous driving or robotics, including working with multi-sensor data (camera, LiDAR, radar)
• Experience with 3D-aware or multi-view consistent generation, or world models
• Experience with large-scale distributed training and inference optimization (e.g., distillation, efficient sampling)
• Peer-reviewed research at conferences such as CVPR, ICCV/ECCV, NeurIPS, ICLR, ICML, or SIGGRAPH

Pay

USD 185,000 - 260,000