Invested in multimodal video understanding as the foundation for meaningful content representations
Skilled at translating embedding system tradeoffs — latency, recall, cost — into product-relevant context that drives cross-functional decisions
Committed to mentoring and knowledge sharing with engineering resources
Effective at operating in a dynamic environment and comfortable taking ownership of project outcomes end-to-end
Responsibilities
Video Understanding & Multimodal Embedding
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
Vector Search & Retrieval Infrastructure
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
Production Systems, Pipeline Engineering & APIs
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
Cross-Functional Leadership & Collaboration
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
Basic 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
Additional Qualifications
Experience with agentic systems and multi-agent orchestration
Knowledge of Diversity & Relevance algorithms such as Maximal Marginal Relevance (MMR) within the re-ranking phase
Background in video codecs, FFmpeg, or low-level video processing pipelines
Awareness with retrieval-augmented generation (RAG) systems
#LI-PG1
Paramount Streaming, a division within Paramount Global, is the home to the company's direct-to-consumer services spanning free and paid in the form of Pluto TV and Paramount+. Pluto TV is the global leader in free ad-supported TV, delivering more than 1,400 global channels and an extensive library of streaming content, including live and original channels. Paramount+, digital subscription video-on-demand and live streaming service, combines live sports, breaking news, and A Mountain of Entertainment™. Paramount+ features an expansive library of original series, hit shows and popular movies across every genre from world-renowned brands and production studios, including SHOWTIME®.
ADDITIONAL INFORMATION
Hiring Salary Range: $139,200.00 - 219,000.00.
The hiring salary range for this position applies to New York, California, Colorado, Washington state, and most other geographies. Starting pay for the successful applicant depends on a variety of job-related factors, including but not limited to geographic location, market demands, experience, training, and education. The benefits available for this position include medical, dental, vision, 401(k) plan, life insurance coverage, disability benefits, tuition assistance program and PTO or, if applicable, as otherwise dictated by the appropriate Collective Bargaining Agreement. This position is bonus eligible.
What We Offer
Attractive compensation and comprehensive benefits packages. Check out our full list of benefits here: https://www.paramount.com/careers/benefits
Generous paid time off.
An exciting and fulfilling opportunity to be part of one of Paramount’s most dynamic teams.
Opportunities for both on-site and virtual engagement events.
Unique opportunities to make meaningful connections and build a vibrant community, both inside and outside the workplace.
Explore life at Paramount: https://www.paramount.com/careers/life-at-paramount
Skydance is an equal opportunity employer (EOE)
We embrace the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit. In line with the laws of the country or region in which the role is based, this includes consideration without regard to race, color, ethnicity, national origin, citizenship status, religion or belief, sex, gender, gender identity or expression, sexual orientation, age, marital status, pregnancy, parenthood, physical or mental disability, genetic information, military or protected veteran status, union status or any other category protected by applicable law.
If you are a qualified individual with a disability, you may request a reasonable accommodation during the job application and/or recruitment process. You can request reasonable accommodations by calling 212.846.5500 or by sending an email to paramountaccommodations@paramount.com. Only messages left for this purpose will be returned.
Nearest Major Market: Manhattan
Nearest Secondary Market: New York City
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