Senior ML Platform Engineer (Rust) – Scalable Inference

UiPath; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Competitive compensation

Hybrid work model

Diversity & inclusion

Job summary

UiPath is seeking a systems-level engineer for its Machine Learning Services team to design, build, and operate the core platform that powers large-scale AI and Document Understanding products. The role focuses on high-performance distributed systems, multi-tenant infrastructure, and robust Kubernetes-based orchestration.

You'll work across Rust, Python, and cloud ecosystems (Azure/AWS/GCP), tackling advanced concurrency and OSS-like reliability while shaping architectural decisions and ensuring

Qualifications

• 5+ years engineering experience designing and building large-scale distributed services.
• Strong proficiency in system languages (Rust, C++, Go) and Python.
• Real-world cloud exposure (Azure/AWS/GCP) and containerization (Docker, Kubernetes).
• Deep understanding of concurrency, multithreading, and asynchronous programming.

Responsibilities

• Design, build, and operate the core MLS platform, including Rust API gateway and Python compute workers.
• Resolve concurrency, performance, and distributed-systems challenges for high-volume workloads.
• Collaborate with product and ML science teams to deploy GenAI models and specialized classifiers.
• Develop storage abstractions over cloud object stores with GC and sharding logic.

Skills

Rust

C++

Go

Python

Distributed systems

Concurrency

Tools

Azure

AWS

GCP

Docker

Kubernetes

ONNX

Job description

UiPath is seeking a systems-level engineer for its Machine Learning Services team to design, build, and operate the core platform that powers large-scale AI and Document Understanding products. The role focuses on high-performance distributed systems, multi-tenant infrastructure, and robust Kubernetes-based orchestration.

You'll work across Rust, Python, and cloud ecosystems (Azure/AWS/GCP), tackling advanced concurrency and OSS-like reliability while shaping architectural decisions and ensuring