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.

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.