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Senior AI Engineer (San Jose)
Senior AI Engineer (San Jose)Harrison Clarke • San Jose, CA, US
Senior AI Engineer (San Jose)

Senior AI Engineer (San Jose)

Harrison Clarke • San Jose, CA, US
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  • [job_card.part_time]
[job_card.job_description]

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.
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