Staff AI Engineer (Search Relevance)

Job Description

Job Description

About Workato

Workato transforms technology complexity into business opportunity. As the leader in enterprise orchestration, Workato helps businesses globally streamline operations by connecting data, processes, applications, and experiences. Its AI-powered platform enables teams to navigate complex workflows in real-time, driving efficiency and agility. Trusted by a community of 400,000 global customers, Workato empowers organizations of every size to unlock new value and lead in today's fast-changing world. Learn how Workato helps businesses of all sizes achieve more at workato.com. Why join us?

Ultimately, Workato believes in fostering a

flexible, trust-oriented culture that empowers everyone to take full ownership of their roles . We are driven by

innovation

and looking for

team players

who want to actively build our company. But, we also believe in

balancing productivity with self-care . That's why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives. If this sounds right up your alley, please submit an application. We look forward to getting to know you! Also, feel free to check out why: Business Insider named us an "enterprise startup to bet your career on"

Forbes' Cloud 100 recognized us as one of the top 100 private cloud companies in the world

Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America

Quartz ranked us the #1 best company for remote workers

Responsibilities

As we work towards building out the Context Layer for the Agentic Enterprise, we are looking for an exceptional Search/AI Engineer with experience in

Search Relevance

to join our growing team. In this role, you will lead the design, development, and optimization of intelligent search systems that leverage machine learning at their core. You'll be responsible for building end-to-end retrieval pipelines that incorporate advanced techniques in query understanding, ranking, and entity recognition. The ideal candidate combines deep expertise in information retrieval and search relevance with hands-on experience applying machine learning to real-world search problems at scale. In this role, you will also be responsible to: Lead the development of advanced

query understanding systems

that parse natural language, resolve ambiguity, and infer user intent.

Design and deploy

learning-to-rank models

that optimize relevance using behavioral signals, embeddings, and structured feedback.

Build and scale robust Entity Recognition pipelines that enhance document understanding, enable contextual disambiguation, and support entity-aware retrieval.

Architect next-gen

search infrastructure

capable of supporting highly dynamic document corpora and real-time indexing.

Create and maintain graph-based knowledge systems

that enhance LLM capabilities through structured relationship data.

Drive improvements in

query rewriting ,

intent classification , and

semantic search , using both statistical and neural methods.

Own the design of

evaluation frameworks

for offline/online relevance testing, A/B experimentation, and continual model tuning.

Collaborate with product and applied research teams to translate user needs into data-informed search innovations

Produce clean, scalable code and influence system architecture and roadmap across the relevance and platform stack.

Requirements

Qualifications / Experience / Technical Skills

Bachelors/Masters/PhD

degree in Statistics, Mathematics or Computer Science, or another quantitative field.

7+ years of backend engineering experience with 3+ years in search, information retrieval, or related fields

Strong proficiency in Python

Hands-on experience with search engines (Opensearch or Elasticsearch)

Strong understanding of information retrieval concepts spanning traditional methods (TF-IDF, BM25) and modern neural search techniques (vector embeddings, transformer models)

Experience with text processing, NLP, and relevance tuning

Experience with relevance evaluation metrics (NDCG, MRR, MAP)

Experience with large-scale distributed systems

Proficiency in Knowledge Graph construction and optimization is a plus.

Strong analytical and problem-solving skills

Soft Skills / Personal Characteristics

Strong communication abilities to explain technical concepts

Collaborative mindset for cross-functional team work

Detail-oriented with strong focus on quality

Self-motivated and able to work independently

Passion for solving complex search problems

(REQ ID: 2326)