Centific is looking to advance visual perception and multimodal reasoning capabilities for AI applications, aiming to bridge the gap between AI creators and industry leaders by enabling safe, scalable AI deployment and reducing GenAI costs and time-to-market.
Requirements
- 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 (e.g., LLaVA‑style, video‑language models), embodied/physical AI , 3D perception.
- Proven ability to move from paper → code → ablation → result with rigorous experiment tracking.
- Experience with video models (e.g., TimeSFormer/MViT/VideoMAE), diffusion or 3D GS/NeRF pipelines, or SLAM/scene reconstruction.
- Prior work on multimodal grounding (referring expressions, spatial language, affordances) or temporal reasoning.
- Familiarity with ROS2 , DeepStream/TAO , or edge inference optimizations (TensorRT, ONNX).
- Scalable training: Ray , distributed data loaders, sharded checkpoints.
Responsibilities
- Build and fine‑tune models for detection, tracking, segmentation (2D/3D), pose & activity recognition, and scene understanding (incl. 360° and multi‑view).
- Train/evaluate vision–language models (VLMs) for grounding, dense captioning, temporal QA, and tool‑use; design retrieval‑augmented and agentic loops for perception‑action tasks.
- Prototype perception‑in‑the‑loop policies that close the gap from pixels to actions (simulation + real data).
- Integrate with planners and task graphs for manipulation, navigation, or safety workflows.
- Curate datasets, author high‑signal evaluation protocols/KPIs, and run ablations that make results irreproducible impossible.
- Package research into reliable services on a modern stack (Kubernetes, Docker, Ray, FastAPI), with profiling, telemetry, and CI for reproducible science.
- Orchestrate multi‑agent pipelines (e.g., LangGraph‑style graphs) that combine perception, reasoning, simulation, and code‑generation to self‑check and self‑correct.
Other
- Ph.D. student in CS/EE/Robotics (or related), actively publishing in CV/ML/Robotics (e.g., CVPR/ICCV/ECCV, NeurIPS/ICML/ICLR, CoRL/RSS).
- Strong software craft: testing, linting, profiling, containers, and reproducibility.
- Public code artifacts (GitHub) and first‑author publications or strong open‑source impact.
