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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Research Engineer: Vision AI / VLM / Physical AI - **Company:** Centific Global Solutions - **Location:** Seattle, WA, United States (Remote available) - **Salary:** $180,000.0 - $240,000.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, 3d Models, 3D Scanning, Artificial Intelligence, Computer Vision, Profiling, Nvidia CUDA, Distributed Data Store, Github, Python (Programming Language), Language Modeling, Open Source Technology, NeRF (Neural Radiance Fields), Management of Software Versions, Pytorch, Retrieval-Augmented Generation, Large Language Models, Deep Learning, Agentic-AI, Fastapi, Isaac Sim, Kubernetes, ONNX (Open Neural Network Exchange) Format, HuggingFace, TensorRT, Mixed Precision, Stable Diffusion, Docker - **Published:** November 7, 2026 - **Apply:** https://startup.jobs/ai-research-scientist-centific-global-solutions-sl-9841373 ## About the Role * Masters/Ph.D 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., * 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. ## Description Computer Vision team as a Research Engineer and dive into the world of 3D reconstruction, scene understanding, and visual AI. You'll explore innovative techniques like those used to transform real-world spaces into immersive 3D models-such as the 3D Reconstruction projects -and work with cutting-edge architectures like VGG-T (Visual Geometry Grounded Transformers), known for advancing deep learning in vision tasks. This role is perfect for those excited to develop AI systems that interpret, reconstruct, and interact with the visual world, using state-of-the-art tools and methodologies. Physical AI Robotics team, where you'll work at the intersection of simulation, robotics, and AI. You'll leverage NVIDIA's Omniverse for advanced 3D simulation and collaboration, Isaac Sim for robotics training and testing, and GR00T for foundation models in robotics. Experience with Holoscan SDK for real-time medical and industrial robotics pipelines, Newton Physics for dynamic simulation, and NVIDIA's NERD for neural robot dynamics will be a plus. This role is ideal for those eager to push the boundaries of AI-driven robotics using state-of-the-art tools and frameworks. 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 tooluse; 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., -LangGraphstyle graphs) that combine perception, reasoning, simulation, and -codeg eneration to -selfc heck and -selfcorrect-. Example Problems You Might Tackle * Long horizon- video understanding (events, activities, causality) from egocentric or 360° video. * 3D scene grounding: linking language queries to objects, affordances, and trajectories. * Fast, privacy preserving perception for -ondevice- or edge inference. * Robust multi-modal evaluation: temporal consistency, open-set detection, uncertainty. * Vision conditioned- policy evaluation in sim (Isaac/MuJoCo) with sim2real stress tests., * Modeling: PyTorch, torchvision/lightning, Hugging Face, OpenMMLab, xFormers * Perception: YOLO/Detectron/MMDet, SAM/Mask2Former, CLIP-style backbones, optical flow * VLM / LLM: Vision encoders + LLMs, RAG for video, toolformer-/agent loops * 3D / Sim: Open3D, PyTorch3D, Isaac/MuJoCo, COLMAP/SLAM, NeRF/3DGS * Systems: Python, FastAPI, Ray, Kubernetes, Docker, Triton/TensorRT, Weights & Biases * Pipelines: LangGraph-like orchestration, data versioning, artifact stores What Success Looks Like * A publishable or open-sourced outcome (with company approval) or a production-ready module that measurably moves a product KPI (latency, accuracy, robustness). * Clean, reproducible code with documented ablations and an evaluation report that a teammate can rerun end-to-end. * A demo that clearly communicates capabilities, limits, and next steps. Why Centific * 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.