Nvidia Deep Learning Jobs in Philadelphia, PA (NOW HIRING)

# Senior AI Engineer, Video Search (Applied Research & Product)DepartmentTechnologyEmployment TypeFull-timeLocationRemote / Hybrid / Conshohocken, PAReports ToDirector of AI## **About ZeroEyes, Inc.**ZeroEyes was founded by former Navy SEALs, self-starters and elite technologists with a mission to reduce the threat and impact of mass shootings and gun-related violence using our best-in-class artificial intelligence (AI) platform that detects visible firearms before there’s a threat. As a member of the ZeroEyes team, you’ll have the unique opportunity to join a forward-facing, purpose-driven company, and your perseverance and individual skill set will become crucial to our mission’s success.## **About the role**We’re hiring a **Senior AI Engineer** to help lead applied research and productionization of **video search**, from natural-language queries to fast, scalable retrieval across archives and live streams. You’ll develop models, pipelines, and high-performance APIs. We value people who care more about **truth than winning arguments**, mentor generously, and take personal responsibility for the organization’s success.## **What you’ll do*** **Contribute to video search stack end-to-end:** dataset curation, model training/fine-tuning, indexing, retrieval APIs, latency/throughput optimization, and real-world evaluation.* **Applied research → production:** Evaluate and integrate **V-JEPA2** style representations for video understanding and retrieval; compare/compose with CLIP/SigLIP/TimeSformer/ViViT/Video-LLMs for NL→video.* **Text–video alignment:** Build query encoders for natural-language search (prompting, adapters, contrastive losses, distillation) and robust negative mining; support multilingual queries.* **Temporal grounding:** Deliver moment-localization and highlight detection (segment-level embeddings, token-aligned pooling, temporal R@K / mAP).* **Indexing at scale:** Stand up vector/search infra (FAISS, Milvus, pgvector, Pinecone) with sharding, HNSW/IVF/ScaNN, hybrid signals (text + metadata + structure).* **Latency & cost:** Optimize preprocessing (frame sampling, shot detection), feature caching, batch inference, and low-latency serving (ONNX Runtime/TensorRT or ROCm paths).* **Cross-GPU strategies:** Design and implement **multi-GPU training and serving**—FSDP/ZeRO, tensor & pipeline parallelism, sharded/streamed decoding, NCCL/RCCL communication tuning, mixed precision/quantization, and elastic autoscaling.* **Quality & evaluation:** Define task-specific metrics (R@K, nDCG, mAP, temporal mAP), build dashboards and AB tests; run bias/robustness checks and failure-mode analyses.* **Security & compliance aware:** Design for privacy, auditability, and clean separation of controlled data; collaborate with platform/DevOps on IaC, CI/CD, and observability.* **Mentor & collaborate:** Level-up adjacent teams (ML Ops, backend, product). Write clear design docs and ADRs; lead design reviews.## **What you’ll bring*** 6–10+ years total; 4+ years applying deep learning to video, vision, or multimodal retrieval with shipped features or products.* Hands-on with **PyTorch** (preferred) and modern video backbones; practical experimentation with **V-JEPA/V-JEPA2** (or JEPA-style self-supervised video objectives).* Strong with **text–image/video** retrieval (CLIP-family, BLIP/BLIP-2, SigLIP, Q-Former/adapters) and contrastive training at scale.* **GPU performance & serving:** mixed precision, ONNX Runtime/TensorRT (NVIDIA) **or** ROCm paths; profiling (nsys/nvprof/rocprof), post-training quantization, distillation.* **Cross-GPU & distributed training:** FSDP/ZeRO, DDP, tensor/pipeline parallelism, NCCL/RCCL, model sharding/checkpointing, and cluster scheduling (Kubernetes + GPU operators).* **ROCm/MIGraphX experience** (preferred): building/optimizing models on AMD GPUs; familiarity with MIOpen, MIGraphX backends, and ROCm toolchain.* Search infrastructure: **FAISS/Milvus/Pinecone/pgvector**, ANN indexes (HNSW/IVF), re-ranking (cross-encoders), and caching strategies.* Data & MLOps: scalable curation, labeling/weak supervision, feature stores, experiment tracking (Weights & Biases/MLflow), CI for ML, and reproducible training.* Solid software engineering: Python (prod-grade), plus a systems language (Go/C++/Rust) or strong willingness to learn; API design; testing; code reviews.* Clear communicator with a bias to measure, publish results, and change direction quickly when the data says so.## **Nice-to-haves*** Temporal detection/segmentation, tracking, re-ID, and multi-camera association.* Video-RAG and structured retrieval (combining embeddings with metadata/knowledge graphs).* On-device or edge inference; WebRTC/RTSP ingest; FFmpeg/GStreamer pipelines.* Experience in regulated or high-assurance environments (FedRAMP/HIPAA/CJIS) and privacy-preserving ML.## **Values*** **No jerks*** **Be authentic*** **Be effective*** **Attention to detail*** **All in, all the time**## **Eligibility*** Must be authorized to work in the U.S. Ability to obtain and maintain a Public Trust or other clearance may be required.## Apply for Senior AI Engineer, Video Search (Applied Research & Product) at ZeroEyes #J-18808-Ljbffr