A

Applied Intuition

Builds software for autonomous vehicles

Senior Machine Learning Engineer - Generative Models

Computer VisionConfirmed live in the last 24 hours
Full-timeSunnyvale, CA, USA$185k - $260k /yr

About the Job

Requirements

  • At least 5 years of experience developing and shipping machine learning 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, such as latent and video diffusion models and 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.
  • Experience with learning-based 3D reconstruction, such as 3D Gaussian Splatting, NeRFs, or feed-forward Gaussian Splatting.
  • A background in computer vision, such as structure from motion, simultaneous localization and mapping, and photogrammetry, and/or computer graphics, such as rendering, rasterization, and ray tracing.
  • Experience shipping machine learning products with clearly defined evaluation metrics and benchmarks.
  • Experience in autonomous driving or robotics, including working with multi-sensor data such as camera, LiDAR, and radar.
  • Experience with 3D-aware or multi-view consistent generation, or world models.
  • Experience with large-scale distributed training and inference optimization, such as distillation and efficient sampling.
  • Programming experience in C++ and/or CUDA.
  • Peer-reviewed research at conferences such as CVPR, ICCV/ECCV, NeurIPS, ICLR, ICML, or SIGGRAPH.
  • A Master's degree or PhD in computer science, physics, robotics, or related fields.

Responsibilities

  • Develop and advance diffusion and video generation models for the Neural Simulation product, bringing the latest research into production to improve realism, controllability, and scalability.
  • Develop controllable generation conditioned on scene layout, camera pose, actors, and trajectories.
  • Develop temporally consistent, multi-camera video generation.
  • Augment real-world logs with new scenarios, actors, and conditions such as weather and lighting.
  • Combine generative models with the 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 the sim-to-real gap.
  • Work with customers to understand their pain points and implement technical solutions in the Neural Simulation product.
  • Collaborate with infrastructure, autonomy, research, and other product teams to deliver end-to-end solutions.
  • Take ownership of critical technical components and influence architecture and product decisions.

Desired Qualifications

  • Experience with learning-based 3D reconstruction, such as 3D Gaussian Splatting, NeRFs, or feed-forward Gaussian Splatting.
  • A background in computer vision, such as structure from motion, simultaneous localization and mapping, and photogrammetry, and/or computer graphics, such as rendering, rasterization, and ray tracing.
  • Experience shipping machine learning products with clearly defined evaluation metrics and benchmarks.
  • Experience in autonomous driving or robotics, including working with multi-sensor data such as camera, LiDAR, and radar.
  • Experience with 3D-aware or multi-view consistent generation, or world models.
  • Experience with large-scale distributed training and inference optimization, such as distillation and efficient sampling.
  • Programming experience in C++ and/or CUDA.
  • Peer-reviewed research at conferences such as CVPR, ICCV/ECCV, NeurIPS, ICLR, ICML, or SIGGRAPH.
  • A Master's degree or PhD in computer science, physics, robotics, or related fields.

About the Company

Applied Intuition

Builds software for autonomous vehicles

Applied Intuition builds software for automakers and operators developing autonomous vehicles and other machines, from passenger cars to industrial equipment. Its Tools for Vehicle Intelligence let engineers test driving behavior in simulated worlds, while Vehicle OS connects software inside a machine with development tools off the vehicle. The Self-Driving System supports driver assistance and fully driverless operation for passenger and commercial vehicles. It sells these systems to businesses and works alongside their engineers to integrate, validate and update vehicle software. Applied Intuition was founded in 2017 and is headquartered in Sunnyvale, California.

Company size
1,001-5,000
Company stage
Series F
Total funding
$1.2B
Headquarters
Sunnyvale, CA
Founded
2017

Recently Posted Jobs

Explore more opportunities at Applied Intuition

See more jobs