Staff AI Engineer
About Workato
Workato delivers enterprise infrastructure for the agentic era, redefining iPaaS and helping enterprises unify data, applications, processes, and AI into a single, governed platform. A leader in Enterprise MCP and trusted by 50% of the Fortune 500, Workato’s cloud-native architecture connects every application, data source, and process to power real-time orchestration at scale. With enterprise-grade security and continuous innovation at its core, Workato provides the trusted foundation for organizations to automate with confidence and operationalize AI across the business. To learn more, visit www.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 forteam 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 for:
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
Bachelor's/Master's/PhD degree in Statistics, Mathematics, 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 teamwork
Detail-oriented with strong focus on quality
Self-motivated and able to work independently
Passion for solving complex search problems
(REQ ID: 2472)