What are the responsibilities and job description for the Senior AI Engineer (Sonoma) position at ExecutivePlacements.com?
Senior AI Engineer - Video Search (Applied Research & Product)
Remote - United States
About The Company
Were partnering with a U.S-based applied AI company building next-generation real-time video understanding systems deployed at scale across enterprise, government, and public environments. The platform combines cutting-edge multimodal AI, vector search, and high-performance inference pipelines to make visual data searchable, interpretable, and actionable in real time.
This is a chance to join a well-funded, mission-driven organization with over tens of thousands of active camera streams and a rapidly growing R&D team pushing the boundaries of multimodal retrieval and AI systems design.
The Role
Were looking for a Senior AI Engineer to lead the applied research and productionization of our video search and retrieval stack - connecting natural-language queries to high-dimensional video representations with real-time performance.
Youll work at the intersection of deep learning research, scalable systems, and GPU-optimized inference, owning models and pipelines end-to-end from training through deployment.
What Youll Do
Remote - United States
About The Company
Were partnering with a U.S-based applied AI company building next-generation real-time video understanding systems deployed at scale across enterprise, government, and public environments. The platform combines cutting-edge multimodal AI, vector search, and high-performance inference pipelines to make visual data searchable, interpretable, and actionable in real time.
This is a chance to join a well-funded, mission-driven organization with over tens of thousands of active camera streams and a rapidly growing R&D team pushing the boundaries of multimodal retrieval and AI systems design.
The Role
Were looking for a Senior AI Engineer to lead the applied research and productionization of our video search and retrieval stack - connecting natural-language queries to high-dimensional video representations with real-time performance.
Youll work at the intersection of deep learning research, scalable systems, and GPU-optimized inference, owning models and pipelines end-to-end from training through deployment.
What Youll Do
- Design and build natural-language-to-video retrieval systems using state-of-the-art architectures (e.g., V-JEPA, CLIP, SigLIP, Video-LLMs, ViViT, TimeSformer).
- Develop temporal localization and video summarization capabilities with fine-grained moment-level embeddings.
- Stand up vector search infrastructure (FAISS, Milvus, pgvector, Pinecone) with optimized sharding, caching, and hybrid retrieval strategies.
- Optimize GPU inference and serving pipelines using ONNX Runtime, TensorRT, or ROCm for low-latency performance.
- Drive multi-GPU training and distributed serving (FSDP, ZeRO, DDP, NCCL/RCCL) with strong understanding of parallelization and quantization techniques.
- Collaborate with MLOps, backend, and product teams to deliver production-ready AI features at scale.
- Define and track key retrieval and relevance metrics (R@K, mAP, nDCG) and run live A/B evaluations.
- Mentor junior engineers, document design decisions, and drive innovation through rigorous experimentation.
- 6-10 years of experience in machine learning or applied AI, with 4 years focused on video understanding, multimodal retrieval, or transformer-based models.
- Proficiency in PyTorch and deep learning frameworks; experience with video backbones, contrastive training, and representation learning.
- Strong understanding of vector databases, ANN search (HNSW, IVF), and embedding pipelines.
- Demonstrated ability to ship high-performance AI systems with GPU optimization, ONNX/TensorRT, or ROCm pipelines.
- Experience with distributed training, CI/CD for ML, and scalable data pipelines (MLflow, W&B, K8s, Docker).
- Excellent communication skills and a collaborative, low-ego approach to problem solving.
- Experience with temporal detection, video tracking, or re-ID.
- Exposure to Video-RAG or structured retrieval (metadata knowledge graph).
- Background in real-time or edge inference systems.
- Interest in privacy-preserving or regulated AI systems.
- Compensation: Competitive base salary bonus equity
- Location: Fully remote (U.S. based)
- Build real-world AI that operates at scale and latency levels few companies ever reach.
- Collaborate with world-class engineers and researchers in a fast-paced, mission-oriented environment.
- Work on deep technical challenges - multimodal search, retrieval, inference optimization - with real-world outcomes.