Senior AI Engineer, Video Search; Applied Research & Product

Position: Senior AI Engineer, Video Search (Applied Research & Product)
Location: Conshohocken

# Senior AI Engineer, Video Search (Applied Research & Product)
Department Technology Employment  Type Full-time

Location

Remote / Hybrid / Conshohocken, PAReports To Director of AI##
** About Zero Eyes, Inc.
** Zero Eyes 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 Zero Eyes 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/Time Sformer/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/Dev Ops 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…