Senior Software Engineer, Machine Learning Services

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?

We're the Machine Learning Services (MLS) team at UiPath—a small, sharp group of senior engineers building the core platform that powers UiPath's large-scale AI and Document Understanding products.
Our world is one of distributed systems, high-throughput model serving, and complex asynchronous training workflows. We're looking for a systems-level engineer who wants to work on the gnarly, foundational problems of a production ML platform. You'll support a system that handles a massive volume of inference requests and orchestrates unattended model training across a diverse landscape of model architectures. This isn't just about gluing APIs together; it's about building the infrastructure that makes it all possible.Our core platform is written in Rust for performance, correctness, and fearless concurrency. ML models and services are primarily in Python. If you're intrigued by the challenges of the software/hardware interface, OS-level optimization, and building robust, multi-tenant distributed systems, you'll fit right in

• Design, build, and operate the core MLS platform. This includes our Rust-based API gateway, Python ML compute workers, and the distributed job queue that orchestrates it all.

• Solve hard concurrency, performance, and distributed systems problems to ensure our platform is bulletproof for high-volume production workloads.

• Work directly with product and ML science teams to understand their needs and build the scalable infrastructure required to bring their models to life—from massive GenAI models to fine-tuned, specialized classifiers.

• Develop our custom-built, content-addressable storage abstraction layer over cloud object stores (GCS, S3, Azure Blob), complete with its own garbage collection and sharding logic.

• Enhance our asynchronous job-queueing system, built from the ground up on the storage layer using compare-and-swap primitives for atomicity. No off-the-shelf message broker could handle our specific needs.

• Dive deep into the entire stack, from Kubernetes and container orchestration, through gRPC-based service communication, to the performance tuning of ONNX-based inference on GPU-accelerated hardware.

• Write clean, efficient, and rigorously tested code. We value simplicity, correctness, and peer review.

• A solid track record (5+ years) of engineering and architecting large-scale, distributed commercial services. Your experience speaks for itself.

• Deep proficiency in a systems-level language (Rust, C++, Go). A willingness and curiosity to become an expert in Rust is essential, as it's the foundation of our core services. Strong Python skills are also critical.

• Real-world experience with cloud ecosystems (Azure, AWS, or GCP) and containerization (Docker, Kubernetes). You should understand how production systems are deployed, monitored, and scaled.

• A firm grasp of concurrency, multithreading, and asynchronous programming. You know the difference between a mutex and a channel, and you know when (and when not) to use them.

• A pragmatic understanding of computer science fundamentals. We care more about your ability to solve real-world problems with data structures and algorithms than your ability to recite them from a textbook.

• An opinion on what makes good code and good architecture, and the ability to articulate it. You should be comfortable challenging assumptions (including our own) and contributing to a culture of continuous improvement.

• You're a builder and a problem-solver at heart.

Nice to Haves (but we can teach you):

• You've already worked with Rust in a production environment.

• Experience with MLOps, particularly the challenges of managing the lifecycle of models in a multi-tenant, high-availability system.

• Familiarity with building ML inference services, model serialization (e.g., ONNX), and GPU programming (CUDA).

• You've built or worked on custom storage or job-queueing systems before and have the scars to prove it.

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.