Technical Intern at Centific in Remote

Summary: Centific is a frontier AI data foundry that empowers enterprise clients with safe, scalable AI deployment. As a Technical Intern, you will work on cutting-edge research in computer vision and embodied intelligence, translating research into production systems that perceive and act in the real world. Responsibilities: - 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; design retrieval‑augmented and agentic loops for perception‑action tasks - Physical AI & Embodiment: 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 - Data & Evaluation at Scale: Curate datasets, author high‑signal evaluation protocols/KPIs, and run ablations that make results irreproducible impossible - Systems & Deployment: Package research into reliable services on a modern stack (Kubernetes, Docker, Ray, FastAPI), with profiling, telemetry, and CI for reproducible science - Agentic Workflows: Orchestrate multi‑agent pipelines (e.g., LangGraph‑style graphs) that combine perception, reasoning, simulation, and code‑generation to self‑check and self‑correct Required Qualifications: - 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 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 Preferred Qualifications: - 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 - Strong software craft: testing, linting, profiling, containers, and reproducibility - Public code artifacts (GitHub) and first‑author publications or strong open‑source impact Required Skills: PyTorch, JAX, Python, CUDA profiling, Mixed-precision training, Computer vision, Vision-language models, Embodied AI, Physical AI, 3D perception, Experiment tracking, Video models, Diffusion pipelines, 3D GS, NeRF, SLAM, Scene reconstruction, Multimodal grounding, Temporal reasoning, ROS2, DeepStream, TAO, Edge inference, TensorRT, ONNX, Ray, Distributed data loaders, Sharded checkpoints, Software testing, Linting, Profiling, Containerization, Reproducibility