Job description

Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co applied.co. We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions ABOUT THE ROLE AND TEAM 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. AT APPLIED INTUITION, YOU WILL: - 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 WE'RE LOOKING FOR SOMEONE WHO HAS: - 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 NICE TO HAVE: - Experience with learning-based 3D reconstruction, such as 3D Gaussian Splatting, NeRFs, or feed-forward Gaussian Splatting - 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 - 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) - 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 Don’t meet every single requirement? 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. You may be just the right candidate for this or other roles. Applied Intuition is an equal opportunity employer and federal contractor or subcontractor. Consequently, the parties agree that, as applicable, they will abide by the requirements of 41 CFR 60-1.4(a), 41 CFR 60-300.5(a) and 41 CFR 60-741.5(a) and that these laws are incorporated herein by reference. These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity or national origin. These regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status or disability. The parties also agree that, as applicable, they will abide by the requirements of Executive Order 13496 (29 CFR Part 471, Appendix A to Subpart A), relating to the notice of employee rights under federal labor laws. FOR US-BASED ROLES: Applied Intuition is committed to providing an accessible and inclusive application and interview experience to applicants who are disabled veterans and other applicants with disabilities or medical conditions. Reasonable accommodations are available, requesting an accommodation will not affect your candidacy in any way, and you are not required to disclose the nature of your disability or medical condition in order to make a request. If you require an accommodation please contact careers@applied.co. We will work with you!

Requirements

Must-haves

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
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

Nice-to-haves

Experience with learning-based 3D reconstruction, such as 3D Gaussian Splatting, NeRFs, or feed-forward Gaussian Splatting
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
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)
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

Application prep

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Keywords to mirror

aimachine learningmlpython

Platform: ashby
Effort: quick

Benefits

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Vision