Senior Machine Learning Engineer - Generative Models

Applied Intuition; Sunnyvale (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Applied Intuition in Sunnyvale, CA is hiring a senior ML engineer to advance diffusion and video-generation models for driving-scene simulation within the Neural Simulation product. You will drive research-to-product efforts, improving realism, temporal consistency, controllability, and evaluation workflows so autonomous systems can be trained and validated against harder simulated conditions.

Collaborate with autonomy, infrastructure, and research teams to productionize methods and contribute

Qualifications

• Experience with diffusion and video-generation models.
• Ability to deploy research into production systems.
• Experience with large-model training or inference workflows.
• Interest in autonomy simulation and evaluation for physical AI systems.

Responsibilities

• Develop diffusion and video-generation models for driving-scene simulation.
• Generate sensor data conditioned on scene layout, camera pose, actors, and trajectories.
• Augment real-world logs with new scenarios, actors, weather, and lighting conditions.
• Define metrics and benchmarks for realism, temporal consistency, controllability, and sim-to-real gap.
• Work with autonomy, infrastructure, research, and customer-facing teams to productionize new methods.

Skills

Generative modeling

Computer vision

ML in production

Large-model training

Job description

What the role actually is

Applied Intuition is hiring a senior ML engineer for the generative-modeling side of its Neural Simulation product. The work focuses on diffusion and video generation models that turn real-world driving data into controllable, photorealistic simulation scenarios.

The role sits at the research-to-product boundary: improving realism, temporal consistency, controllability, and evaluation workflows so autonomy teams can train and validate systems against harder simulated conditions.

What you would work on

• Develop diffusion and video-generation models for driving-scene simulation
• Generate sensor data conditioned on scene layout, camera pose, actors, and trajectories
• Augment real-world logs with new scenarios, actors, weather, and lighting conditions
• Define metrics and benchmarks for realism, temporal consistency, controllability, and sim-to-real gap
• Work with autonomy, infrastructure, research, and customer-facing teams to productionize new methods

What they are asking for

• Strong ML experience in generative modeling, computer vision, or related areas
• Ability to bring recent research into production systems
• Experience with large-model training or inference workflows
• Interest in autonomy simulation and evaluation for physical AI systems

Why this one is worth a look

Simulation quality is becoming a core constraint for autonomy teams. This role is interesting because it applies fast-moving generative-model research to concrete validation problems for vehicles and other moving machines.