Senior AI Engineer (Alameda)

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

• 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.
  
  

What Were Looking For

• 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.
  
  

Nice-to-Haves

• 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 & Logistics

• Compensation: Competitive base salary bonus equity
• Location: Fully remote (U.S. based)
  
  

Why Join

• 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.