Senior Software Engineer - HLS
Listed on 2026-05-26
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Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Software Engineer
Life at Ui Path
The people at UiPath believe in the transformative power of automation to change how the world works. We’re committed to creating category‑leading enterprise software that unleashes that power.
To make that happen, we need people who are curious, self‑propelled, generous, and genuine. People who love being part of a fast‑moving, fast‑thinking growth company. And people who care—about each other, about UiPath, and about our larger purpose.
Could that be you?
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 DoBuild 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, Git Hub 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
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 (Lang Graph, Lang Chain, or equivalent), durable workflows, retries, and long‑running execution patterns
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
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
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,…
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