Senior Machine Learning Engineer

Description

The company, Inc. is powering the future of physical AI. The company 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. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co .

We are an in-office company, and our expectation is that full-time employees primarily work from their the company 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 the company, 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

Requirements

• 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.
• FOR US-BASED ROLES: the company 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!