Senior MLE, Embedding & Search

WHO WE ARE

We are looking for talent to build the global standard for video understanding AI together!

Twelve Labs is creating the world's best video-specific AI models that effectively process vast amounts of video data, providing video-specialized search, analysis, summarization, and insight generation capabilities.

Major sports leagues use Twelve Labs models to quickly and accurately select highlights from vast game footage, providing hyper-personalized viewing experiences. Domestic integrated control centers efficiently search CCTV footage with Twelve Labs to respond quickly to crisis situations, and major broadcasters and studios worldwide utilize Twelve Labs models for content creation for billions of viewers.

Twelve Labs is a Deep Tech startup with offices in San Francisco and Seoul, named one of the top 100 AI startups globally by CB Insights for four consecutive years. We have secured over $110 million in funding from world-class VCs and corporations such as NVIDIA, NEA, Index Ventures, Databricks, and Snowflake, and our AI models developed in Korea are the only ones serviced through Amazon Bedrock. We build innovative products with exceptional colleagues and grow with customers worldwide.

Twelve Labs works around the following core values:

• An attitude of honesty and reflection towards oneself and the team
• Perseverance and humility, unafraid of failure and feedback
• A mindset of enhancing team capabilities through continuous learning

If you enjoy the process of growing while solving challenging problems together, the opportunity is here at Twelve Labs.

ABOUT THE TEAM

Our team is responsible for Twelve Labs' multimodal representation learning and production serving. We train models that unify various modalities such as video, audio, and text into a single embedding space, and stably serve them through production systems used by thousands of customers worldwide.

We conduct experiments on multimodal embedding models in large-scale distributed training environments and are responsible for the end-to-end process of converting research results into real-time inference systems. With access to world-class GPU resources like NVIDIA B300, we minimize the transition period from research to production.

In a short development cycle where research results are delivered to customers worldwide within months, we collaborate closely with the Research, Product, and Infrastructure teams to create technological impact.

ABOUT THE ROLE

As a Senior MLE on the Embedding & Search team, you will own and build key components of TwelvaLabs' search and retrieval platform — the systems that combine vector search, lexical retrieval, and reranking into fast, accurate, and scalable search experiences for our customers.

This is a systems-heavy ML engineering role at the intersection of information retrieval, ML serving, and distributed systems. We're looking for a strong engineer who can take well-scoped problems with moderate ambiguity, break them down into concrete milestones, and deliver reliable, performant solutions.

IN THIS ROLE, YOU WILL

• Own and build core subsystems of our search platform on EKS — spanning vector indexing (ANN), lexical retrieval, hybrid fusion, reranking, and temporal (segment-level) search
• Optimize retrieval performance at million to billion-scale across both vector and lexical paths
• Develop and maintain production microservices across the search stack
• Collaborate with the research/training team to co-evolve embeddings, reranking models, and retrieval strategies
• Implement and maintain evaluation frameworks for search quality (recall, precision, latency, relevance)
• Work cross-functionally with platform/infra and product teams to ship search capabilities end-to-end

YOU MAY BE A GOOD FIT IF YOU HAVE

• 6–8 years building production ML systems, with emphasis on search, retrieval, or recommendation
• Strong software engineering skills in Python; Go experience is a plus
• Hands-on experience with ML model serving and inference optimization in production (e.g., KServe, Triton, Ray Serve)
• Experience with information retrieval systems — embedding-based search, lexical search (BM25/Elasticsearch), or hybrid retrieval
• Proficiency with data pipelining and orchestration (Spark, Ray, Airflow, Kubeflow, or similar)
• Strong Kubernetes experience and familiarity with databases, vector databases, and search engines
• Solid distributed systems and async programming fundamentals

PREFERRED QUALIFICATIONS

• Good English communication skills (verbal and written)
• Experience with multimodal or video search/retrieval systems
• Familiarity with temporal indexing or segment-level retrieval (shot boundary detection, scene search)
• Experience with hybrid retrieval strategies (rank fusion, reranking models, score normalization)
• Experience with ANN index tuning at scale
• Experience building services with high-demand SLAs