Senior Software Engineer -AI

UiPath•Bellevue, WA

21h

About The Position

UiPath is seeking a Senior Software Engineer to build foundational technologies for its enterprise AI platform. The role works at the intersection of distributed systems, generative AI, and enterprise software, developing capabilities that help AI agents and intelligent applications operate reliably, learn continuously, and deliver business outcomes at scale. It involves hands-on engineering across AI evaluations, agent memory, continuous learning, orchestration, intelligent document processing, and retrieval systems, turning emerging AI technologies into production-ready enterprise solutions. The engineer will build and operate production-grade AI applications and cloud-native services, own solutions from ambiguous requirements through design, implementation, testing, deployment, and ongoing operations, and contribute across frontend, backend services, APIs, and product experiences. The role partners with product, engineering, and customer-facing teams, influences technical decisions and architecture, mentors other engineers, and helps establish best practices for reliable AI-powered software. UiPath describes its workplace as inclusive and notes that many roles allow flexibility in when and where work is done, depending on business and role needs.

Requirements

Strong professional software engineering experience building, shipping, and operating complex, production-grade applications, ideally in enterprise SaaS or large-scale cloud platforms.
Deep understanding of distributed systems, backend architecture, microservices, APIs, and cloud-native development.
Hands-on experience building and deploying production generative AI applications, such as LLM-powered products, RAG systems, AI agents, intelligent document processing, or AI-driven enterprise workflows.
Strong programming skills in Python, C#, or Java.
Experience designing systems for scalability, reliability, performance, security, and maintainability.
Understanding of practical generative AI engineering challenges, including context management, model behavior, retrieval quality, evaluation, latency, and cost optimization.
Ability to navigate ambiguous technical challenges, make sound architectural decisions, and independently deliver high-quality solutions.
Strong product instincts and ability to understand the end-to-end customer experience beyond individual services or components.
Excellent collaboration and communication skills, including effectiveness across engineering, product, and customer-facing teams.

Nice To Haves

Experience with TypeScript or interest in developing full-stack capabilities.
Experience building AI evaluation frameworks, online/offline evaluation pipelines, or feedback-driven improvement systems.
Experience with agent memory, persistent context, continuous learning, reinforcement learning from human feedback (RLHF), or other AI optimization techniques.
Experience developing agentic systems involving orchestration, tool calling, planning, state management, or multi-step execution.
Deep experience with document intelligence, including OCR, parsing, chunking strategies, embeddings, vector databases, hybrid search, reranking, and grounding.
Experience improving production AI reliability and observability, including tracing, quality monitoring, failure analysis, and evaluation-driven development.
Experience building enterprise AI platforms, developer infrastructure, search and retrieval systems, or large-scale cloud services.
Experience collaborating directly with customers to translate complex business requirements into production software.

Responsibilities

Design, develop, and scale services supporting AI agents, evaluations, memory, context management, continuous learning, and intelligent document processing.
Build and operate LLM-powered applications, RAG pipelines, agentic workflows, and document-understanding solutions for customer problems.
Develop highly available, scalable, cloud-native services with asynchronous processing, concurrency, fault tolerance, security, and performance in mind.
Develop evaluation frameworks, observability, monitoring, and feedback mechanisms to improve AI quality, accuracy, reliability, and performance.
Build continuous-learning systems across modalities, including agents, ontologies, and deterministic logic.
Own engineering solutions end to end, from ambiguous requirements and technical design through implementation, testing, deployment, and production operations.
Address enterprise-scale challenges such as multi-tenancy, permissions, retries, idempotency, cost efficiency, latency, and resilience.
Contribute across frontend, backend services, APIs, and product-facing experiences.
Partner with Product Managers, Forward Deployed Engineers, engineering teams, and customers to translate customer challenges into scalable product capabilities.
Influence technical decisions and architecture discussions, mentor fellow engineers, and help establish engineering best practices.