Role Description
Are you pushing the frontier of computer vision, multimodal large models, and embodied/physical AI—and have the publications to show it? Join us to translate cutting-edge research into production systems that perceive, reason, and act in the real world.
We are building state-of-the-art Vision AI across 2D/3D perception, egocentric/360° understanding, and multimodal reasoning. As an AI Research Engineer, you will own high-leverage experiments from paper → prototype → deployable module in our platform.
We are seeking passionate Engineers to join our cutting-edge labs, you could be part of:
-
Computer Vision team:
Dive into the world of 3D reconstruction, scene understanding, and visual AI. Explore innovative techniques like those used to transform real-world spaces into immersive 3D models.
-
Physical AI Robotics team:
Work at the intersection of simulation, robotics, and AI. Leverage NVIDIA’s Omniverse for advanced 3D simulation and collaboration.
What You’ll Do:
-
Advance Visual Perception:
Build and fine-tune models for detection, tracking, segmentation (2D/3D), pose & activity recognition, and scene understanding (incl. 360° and multi-view).
-
Multimodal Reasoning with VLMs:
Train/evaluate vision-language models (VLMs) for grounding, dense captioning, temporal QA, and tool use.
-
Physical AI & Embodiment:
Prototype perception-in-the-loop policies that close the gap from pixels to actions.
-
Data & Evaluation at Scale:
Curate datasets, author high-signal evaluation protocols/KPIs, and run ablations.
-
Systems & Deployment:
Package research into reliable services on a modern stack (Kubernetes, Docker, Ray, FastAPI).
-
Agentic Workflows:
Orchestrate multi-agent pipelines that combine perception, reasoning, simulation, and code generation.
Example Problems You Might Tackle:
-
Long horizon video understanding from egocentric or 360° video.
-
3D scene grounding: linking language queries to objects, affordances, and trajectories.
-
Fast, privacy-preserving perception for on-device or edge inference.
-
Robust multi-modal evaluation: temporal consistency, open-set detection, uncertainty.
-
Vision conditioned-policy evaluation in simulation with sim2real stress tests.
Qualifications
-
Masters/Ph.D in CS/EE/Robotics (or related), actively publishing in CV/ML/Robotics.
-
Strong PyTorch (or JAX) and Python; comfort with CUDA profiling and mixed precision training.
-
Demonstrated research in computer vision and at least one of: VLMs, embodied/physical AI, 3D perception.
-
Proven ability to move from paper → code → ablation → result with rigorous experiment tracking.
Requirements
-
Experience with video models (e.g., TimeSFormer/MViT/VideoMAE), diffusion or 3D GS/NeRF pipelines, or SLAM/scene reconstruction.
-
Prior work on multimodal grounding or temporal reasoning.
-
Familiarity with ROS2, DeepStream/TAO, or edge inference optimizations.
-
Scalable training: Ray, distributed data loaders, sharded checkpoints.
-
Strong software craft: testing, linting, profiling, containers, and reproducibility.
-
Public code artifacts (GitHub) and first-author publications or strong open source impact.
Benefits
-
Real impact: Your research ships—powering core features in our MVPs and products.
-
Mentorship: Work closely with our Principal Architect and senior engineers/researchers.
-
Velocity + Rigor: We balance top-tier research practices with pragmatic product focus.
-
Salary: $140K - $150K
