Senior Machine Learning Engineer, Content Engineering

Responsibilities

• Design and build embedding pipelines for video content metadata and clip-level representation
• Design collection and vector schemas to shape data structure, indexing behavior, and retrieval performance under scale and modality complexity
• Lead the transition from traditional feature engineering to a vector-centric "context-first" architecture, through compositional queries and by designing high-dimensional hyper-vector representations that unify visual, textual, and behavioral signals
• Design offline/online evaluation frameworks (e.g., nDCG, MRR, Recall@K) specifically for multimodal alignment, ensuring content embeddings match search intent
• Build hybrid retrieval systems that combine vector similarity search with lexical search and reranking layers to deliver fast, accurate, and scalable performance at production scale
• Engineer the retrieval layer to capture nuanced user-content relationships that model training alone cannot surface, combining multimodal embeddings to improve recommendation depth at scale
• Implement query-time optimizations including caching, filtering, and index sharding strategies
• Tune vector quantization strategies (PQ, SQ, Binary Quantization) to reduce memory footprint and improve search throughput without compromising retrieval precision
• Own performance SLAs and monitor retrieval systems for latency, throughput, recall, and cost efficiency
• Build and maintain scalable batch and streaming pipelines, with logging, metrics, and alerting to surface anomalies and maintain observability
• Process content at scale using distributed frameworks such as Spark or Ray
• Architect and build scalable integration layers on top of vector databases, exposing robust APIs and services for similarity search, hybrid retrieval, and metadata filtering
• Own model versioning and embedding migration strategies, building compatibility tooling that prevents embedding drift from degrading retrieval quality across model upgrades
• Collaborate with backend and platform teams to ensure interoperability with upstream data pipelines and integration with downstream personalization and discovery surfaces
• Communicate technical system behavior, tradeoffs, and recommendations clearly to both technical and non-technical stakeholders
• Mentor direct reports, providing technical guidance in multimodal ML, vector retrieval, and production systems design
• Take ownership of project outcomes from scoping through delivery in a dynamic environment, proactively identifying and mitigating risks across video processing, metadata, and indexing workflows

Qualifications

• 5–8+ years of experience in machine learning engineering, with a focus on production ML systems
• Expertise in multimodal ML, including experience with video, image, and/or audio embedding models
• Deep knowledge of vector embedding generation, storage and retrieval, with preference for hands-on Qdrant experience (FAISS, Pinecone, Pgvector, AlloyDB or similar also considered)
• Strong Python proficiency; Java is a plus
• Demonstrated experience building and operating data pipelines at scale, including batch and streaming ingestion workflows
• Solid understanding of hybrid retrieval systems: vector search, lexical search, and reranking
• Proven ability to communicate technical concepts clearly and partner effectively with product and engineering teams
• Track record of mentoring engineers and leading technical decisions in a team setting