About the RoleJoin the HLS team and help build UiPath's most ambitious product - an AI agentic orchestration platform that's been recognized as our Invention of the Year for two consecutive years. As a Senior Software Engineer, you'll be at the forefront of our Act 2 strategy, building the infrastructure that allows enterprises to seamlessly orchestrate AI agents, robots, and human-in-the-loop workflows to achieve critical business outcomes.
What You'll Do- Build and Scale: Design, develop, and maintain backend services for HLS products using AI Coding Assistant for building distributed systems
- Hands-On Development: Write production-quality code daily, leveraging AI tools (Claude, GitHub Copilot, etc.) to enhance your development workflow
- Prototype and Ship: Take ideas from concept to working demo in days, validate with real customers, and turn the ones that prove value into production systems.
- Measure what matters: Design evaluations, measure quality against real customer data, and understand when metrics mislead. Our systems are often non-deterministic. Responsible shipping means measuring honestly, not chasing vanity numbers.
- Customer-Centric Engineering: Deeply understand who will use the features you build and why they need them, translating customer needs into elegant technical solutions
- Ensure Reliability: Contribute to building highly scalable, reliable distributed systems that enterprises depend on
- On-Call Rotation: Participate in on-call rotation (approximately one week every 2-3 months) to ensure system reliability
What We're Looking For:Required Qualifications- Builder mindset - you enjoy turning ideas into working systems and taking ownership from concept through production
- Strong backend engineering in Python and/or C# - most HLS product development is Python (agents, APIs, ETL), while core platform work includes C#. Willingness to work across both is important.
- Experience building production AI systems - LLMs, tool-calling, structured outputs, orchestration, and evaluation of model behavior in real-world workflows
- Experience with agentic or workflow-based systems - multi-agent architectures, orchestration frameworks (LangGraph, LangChain, or equivalent), durable workflows, retries, and long-running execution patterns
- Experience with healthcare systems or standards such as Epic, Cerner, X12 EDI (835/837), CARC/RARC denials, HL7/FHIR, ICD-10/HCPCS, prior authorization, claims, or revenue cycle workflows
- Comfortable working with non-deterministic systems - debugging failures, evaluating quality empirically, and distinguishing between model, prompt, and data issues
- Distributed systems experience - designing resilient systems with idempotency, replayability, state management, and long-running jobs
- Strong analytical thinking - able to reason quantitatively about system quality and make sound decisions using imperfect signals
- Comfortable with ambiguity and rapid prototyping - able to move from idea to proof-of-concept quickly and iterate based on customer feedback
- Healthcare domain experience
Nice to Have- Data science and evaluation
- Experience with applied ML techniques such as classification, anomaly detection, ranking, or predictive modeling
- Experience evaluating AI systems using experimentation, metrics, and empirical analysis to improve quality and reliability
- Data and retrieval systems
- Experience with large-scale data platforms (Snowflake, columnar warehouses, denormalized data models)
- Familiarity with retrieval-heavy architectures, RAG systems, or citation-grounded AI workflows
- Engineering for trustworthy AI
- Experience blending deterministic and probabilistic systems (rules engines + AI)
- Experience designing eval-driven systems, regression testing for LLM outputs, or human-in-the-loop review workflows
- PHI/HIPAA-aware engineering and multi-tenant system design
- Platform and product development
- Frontend familiarity (React, TypeScript)
- Workflow orchestration platforms (Temporal, Airflow, Maestro, BPMN, Step Functions)
- Kubernetes and distributed infrastructure
- Experience in startups, applied research, or 01 product environments
- Open-source contributions
How We WorkHLS operates like an internal startup inside UiPath: small teams, fast iteration, and real customers in the loop. We build quickly, test ideas with users, and turn the best ones into products. Some ideas won't make it - that's expected. You'll work in short cycles, demo frequently, and adapt as we learn. We care more about solving the right problem than protecting a plan that no longer fits. If you enjoy ambiguity, rapid feedback, and building where product boundaries move fast, you'll likely thrive here.
About the TeamYou'll be working with Lax and the Maestro backend team to build the core orchestration engine that powers enterprise AI automation. This is a high-visibility, high-impact role on UiPath's biggest strategic bet.
Why Join Us- Work on cutting-edge AI technology that's shaping the future of enterprise automation
- Be part of a team building Times top 100Invention of the Year (2 years running)
- Collaborate with talented engineers who value rapid iteration and customer impact
- Contribute to UiPath's Act 2 strategy - our most important product initiative
Maybe you don't tick all the boxes above-but still think you'd be great for the job? Go ahead, apply anyway. Please. Because we know that experience comes in all shapes and sizes-and passion can't be learned.
Many of our roles allow for flexibility in when and where work gets done. Depending on the needs of the business and the role, the number of hybrid, office-based, and remote workers will vary from team to team. Applications are assessed on a rolling basis and there is no fixed deadline for this requisition. The application window may change depending on the volume of applications received or may close immediately if a qualified candidate is selected.
