Zeroeyes
Senior Application Engineer (Web & Cross-Platform)
Zeroeyes, Conshohocken, Pennsylvania, United States
Principal Engineer, DevOps & Infrastructure
Location:
Remote/Hybrid/On-site — Conshohocken, PA
Employment:
Full-time
Reports to:
Director 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). Experience in regulated or high-assurance environments (FedRAMP/HIPAA/CJIS) and privacy-preserving ML. Values
All in, all the time Must be authorized to work in the U.S. Ability to obtain and maintain a Public Trust or other clearance may be required. APPLY NOW
THANK YOU FOR YOUR DESIRE TO BECOME A MEMBER OF THE ZEROEYES TEAM!
Please create an application account by filling out our application form. We look forward to reviewing your application. Submit all documents/screenshots in PDF format. Personal Statement: Please provide a personal statement (maximum 200 words) that explains why you’re a great fit for our mission and the position you’re applying for. Try to use specific examples. Please use this format when naming your files: For your Resume: LastName_FirstName_Resume For your Personal Statement: LastName_FirstName_PersonalStatement Examples: Doe_John_Resume, Doe_John_PersonalStatement Our team consists of former Navy SEALs, military personnel and technology experts with a passion for contributing to the greater good. We're subject matter experts in the fields of weaponry and gun-detection technology, and our sole focus is creating easy-to-use, non-invasive software to help prevent mass shootings and gun-related violence. #J-18808-Ljbffr
Remote/Hybrid/On-site — Conshohocken, PA
Employment:
Full-time
Reports to:
Director 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). Experience in regulated or high-assurance environments (FedRAMP/HIPAA/CJIS) and privacy-preserving ML. Values
All in, all the time Must be authorized to work in the U.S. Ability to obtain and maintain a Public Trust or other clearance may be required. APPLY NOW
THANK YOU FOR YOUR DESIRE TO BECOME A MEMBER OF THE ZEROEYES TEAM!
Please create an application account by filling out our application form. We look forward to reviewing your application. Submit all documents/screenshots in PDF format. Personal Statement: Please provide a personal statement (maximum 200 words) that explains why you’re a great fit for our mission and the position you’re applying for. Try to use specific examples. Please use this format when naming your files: For your Resume: LastName_FirstName_Resume For your Personal Statement: LastName_FirstName_PersonalStatement Examples: Doe_John_Resume, Doe_John_PersonalStatement Our team consists of former Navy SEALs, military personnel and technology experts with a passion for contributing to the greater good. We're subject matter experts in the fields of weaponry and gun-detection technology, and our sole focus is creating easy-to-use, non-invasive software to help prevent mass shootings and gun-related violence. #J-18808-Ljbffr