Principal Applied Scientist

at UiPath · Enterprise · Bellevue, WA · Engineering

Principal Applied Scientist at UiPath focused on leading the architecture, research, and productization of next-generation ML systems for agent-based automation. The role involves defining technical strategy, architecting and deploying advanced ML systems (LLM fine-tuning, multimodal pipelines, agent orchestration), and leading ML infrastructure for training, inference, and MLOps. It also includes researching state-of-the-art techniques and establishing evaluation frameworks for agentic systems, with a strong emphasis on technical leadership and mentoring.

What you'd actually do

  1. Define and drive technical strategy for agent-based automation, including how autonomous agents use LLMs, reinforcement learning, simulation environments, tool use, and multi-step reasoning to integrate with the UiPath platform.
  2. Architect, prototype, and deploy advanced ML and AI systems, covering LLM fine-tuning, multimodal pipelines, computer-use modeling, agent orchestration frameworks, and decision-making systems.
  3. Lead the design and implementation of ML infrastructure and services for model training, fine-tuning, large-scale inference, model serving, monitoring, drift detection, continuous learning loops, and ML operations for agentic systems.
  4. Partner closely with product, engineering, design, and go-to-market teams to translate research advances into customer-facing capabilities.
  5. Research state-of-the-art techniques in prompting, retrieval-augmented generation, chain-of-thought, tool use, long-term memory, and RL or imitation learning for agent behavior, and apply them to automation workflows.

Skills

Required

  • Python
  • large language models
  • foundation models
  • fine-tuning
  • data preparation
  • inference optimization
  • model evaluation
  • large datasets
  • distributed training
  • distributed inference
  • scalable architecture design
  • optimization for latency
  • optimization for throughput
  • optimization for cost
  • ML frameworks
  • ML libraries
  • integrating ML models into large-scale software systems

Nice to have

  • reinforcement learning
  • simulation environments
  • tool use
  • multi-step reasoning
  • multimodal pipelines
  • computer-use modeling
  • agent orchestration frameworks
  • decision-making systems
  • model serving
  • monitoring
  • drift detection
  • continuous learning loops
  • ML operations
  • prompting
  • retrieval-augmented generation
  • chain-of-thought
  • long-term memory
  • imitation learning
  • offline evaluation
  • human-in-the-loop feedback
  • A/B testing
  • publications
  • open-source contributions
  • conference participation
  • collaboration with academia
  • ecosystem partners

What the JD emphasized

  • 10+ years of industry experience in machine learning, including substantial experience building and operating ML systems in production.
  • Proven experience with large language models or foundation models, including fine-tuning, data preparation, inference optimization, or model evaluation.
  • Experience with large datasets, distributed training or inference, scalable architecture design, and optimization for latency, throughput, and cost.
  • Demonstrated technical leadership in mentoring, influencing strategy, balancing research with delivery, and driving high-impact outcomes.

Other signals

  • leading architecture and productization of next-generation ML systems
  • bridging deep research with deployment at scale
  • shaping the future of enterprise automation
Read full job description

Life at UiPath

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?

As a Principal Applied Scientist, you will lead the architecture, research, and productization of these next-generation ML systems, bridging deep research with deployment at scale and shaping the future of enterprise automation.

What You Will Do • Define and drive technical strategy for agent-based automation, including how autonomous agents use LLMs, reinforcement learning, simulation environments, tool use, and multi-step reasoning to integrate with the UiPath platform. • Architect, prototype, and deploy advanced ML and AI systems, covering LLM fine-tuning, multimodal pipelines, computer-use modeling, agent orchestration frameworks, and decision-making systems. • Lead the design and implementation of ML infrastructure and services for model training, fine-tuning, large-scale inference, model serving, monitoring, drift detection, continuous learning loops, and ML operations for agentic systems. • Partner closely with product, engineering, design, and go-to-market teams to translate research advances into customer-facing capabilities. • Research state-of-the-art techniques in prompting, retrieval-augmented generation, chain-of-thought, tool use, long-term memory, and RL or imitation learning for agent behavior, and apply them to automation workflows. • Establish best practices, frameworks, and metrics for evaluating agentic systems, including offline evaluation, simulation environments, human-in-the-loop feedback, A/B testing, and cost, latency, and quality analysis. • Serve as a technical leader and mentor across ML engineering, data science, and software engineering, fostering a culture of experimentation, reproducibility, versioning, and rigorous evaluation. • Represent UiPath in the broader community through publications, open-source contributions, conference participation, and collaboration with academia or ecosystem partners.

What We Are Looking For • Advanced degree (MS or PhD preferred) in Computer Science, Machine Learning, AI, or a related field, or equivalent experience. • 10+ years of industry experience in machine learning, including substantial experience building and operating ML systems in production. • Deep expertise in modern ML frameworks and libraries, strong proficiency in Python, and experience integrating ML models into large-scale software systems. • Proven experience with large language models or foundation models, including fine-tuning, data preparation, inference optimization, or model evaluation. • Experience with large datasets, distributed training or inference, scalable architecture design, and optimization for latency, throughput, and cost. • Excellent communication skills with the ability to translate complex ML concepts to product and business audiences. • Demonstrated technical leadership in mentoring, influencing strategy, balancing research with delivery, and driving high-impact outcomes.

#LI-MH1

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.

We value a range of diverse backgrounds, experiences and ideas. We pride ourselves on our diversity and inclusive workplace that provides equal opportunities to all persons regardless of age, race, color, religion, sex, sexual orientation, gender identity, and expression, national origin, disability, neurodiversity, military and/or veteran status, or any other protected classes. Additionally, UiPath provides reasonable accommodations for candidates on request and respects applicants' privacy rights. To review these and other legal disclosures, visit our privacy policy.