Senior Machine Learning Engineer - 3D Reconstruction

Applied Intuition; Sunnyvale (CA); Sur place; USD 180 000 - 240 000; Plein temps

Résumé du poste

Applied Intuition in Sunnyvale is seeking a senior ML engineer to advance the reconstruction tech behind Neural Simulation. You will transform real driving data into photorealistic environments used to train, test, and validate autonomy systems.

You will productionize Gaussian splatting, NeRFs, and feed-forward reconstruction, while supporting camera and LiDAR rendering for autonomy simulation workflows and collaborating with multiple teams to raise simulation fidelity.

Qualifications

• Strong background in 3D computer vision, graphics, machine learning, or neural rendering
• Experience moving research methods into production systems
• Ability to reason about reconstruction quality, throughput, and customer-facing simulation needs
• Interest in physical AI, autonomy validation, and large-scale fleet-data pipelines

Responsabilités

• Improve learning-based reconstruction methods for large, dynamic driving scenes
• Productionize techniques such as Gaussian splatting, NeRFs, and feed-forward reconstruction
• Support camera and LiDAR rendering for autonomy simulation workflows
• Build evaluation metrics for reconstruction fidelity and sim-to-real gap
• Collaborate with autonomy, infrastructure, research, and product teams on end-to-end simulation quality

Connaissances

3D computer vision

Neural rendering

Production engineering

Autonomy simulation

Outils

Gaussian splatting

NeRFs

LiDAR rendering

Camera rendering

Description du poste

What the role actually is

Applied Intuition is hiring a senior ML engineer to improve the reconstruction technology behind Neural Simulation. The team turns real driving data into photorealistic environments that customers use to train, test, and validate autonomy systems.

The posting calls out 3D Gaussian Splatting, NeRFs, dynamic actor reconstruction, scene editing, novel-view synthesis, and multi-sensor rendering. It is a production research role for simulation fidelity and scale.

What you would work on

• Improve learning-based reconstruction methods for large, dynamic driving scenes
• Productionize techniques such as Gaussian splatting, NeRFs, and feed-forward reconstruction
• Support camera and LiDAR rendering for autonomy simulation workflows
• Build evaluation metrics for reconstruction fidelity and sim-to-real gap
• Collaborate with autonomy, infrastructure, research, and product teams on end-to-end simulation quality

What they are asking for

• Strong background in 3D computer vision, graphics, machine learning, or neural rendering
• Experience moving research methods into production systems
• Ability to reason about reconstruction quality, throughput, and customer-facing simulation needs
• Interest in physical AI, autonomy validation, and large-scale fleet-data pipelines

Why this one is worth a look

The role is a good signal of where autonomy tooling is moving: better simulation is not just prettier scenes, but a way to close validation gaps using real-world fleet data. The caveat is that this is deeply technical product infrastructure rather than a pure robotics lab role.