Manufacturing is entering its biggest transformation in decades.
For years, we’ve helped manufacturers digitize their operations through a platform used by leading manufacturers across Europe and North America. From digital work instructions and quality management to traceability, skills and operational excellence, we’ve helped thousands of operators perform their work better, faster and safer.
But we’re only getting started.
The next generation of manufacturing won’t be driven by software alone. It will be powered by AI that fundamentally changes how work gets done on the factory floor. We’re building a future where work instructions are generated automatically from engineering data, AI agents assist operators in real time, quality documentation is created autonomously, and repetitive administrative work simply disappears.
We’re not digitizing existing processes anymore. We’re redesigning manufacturing execution from the ground up.
Founded in 2016, Azumuta has grown into one of Europe’s leading Manufacturing SaaS scale-ups, working with some of the world’s most innovative manufacturers across industries including aerospace & defense, industrial machinery, automotive, electronics and medical devices.
We’re building what manufacturing will look like over the next decade. If that excites you, you’ll fit right in.
As a Medior Product Engineer, your role will focus on shaping the Azumuta product as part of a team, with coaching and growing ownership.
Strong full-stack engineers are very welcome. Heavy backend specialists are equally welcome, because much of what we are building is deeply backend-shaped: AI agents and orchestration, structured execution data at granular depth, deep integrations with the IT systems our customers already run, multi-tenant and on-prem deployment topologies.
Specialization in the right area is what makes a senior engineer exceptional, rather than spread thin.
Founded in 2016, Azumuta is a Manufacturing SaaS scale-up providing digital solutions to factories worldwide. We are building the execution platform for hybrid manufacturing: the system that orchestrates work on the shop floor across humans, AI agents, and robotic systems.
And the bet on top is bigger: vision-based step verification, agentic execution supervision, autonomous continuous improvement, factory replay for model training. None of this is slideware. It is what we are building over the coming years.
Work instructions from video. A process engineer records a short video of an operation; our system produces a structured, version-controlled instruction the team can review and ship.
A continuous-improvement agent. Dashboards already show deviations. We want an agent that proposes evidence-backed instruction changes: not "cycle time is up on station 3" but "rework concentrates on step 7, here is the fix and the data behind it."
Multi-level BOMs and nested assemblies. Industrial products are deeply nested; we handle the leaves but not the tree. Data model, variant rollup, integration with engineering data, routing. Foundational, and what unlocks our most variant-heavy customers.
Our core stack today is Node.js, TypeScript, React, and MongoDB on cloud infrastructure.
The upcoming work stretches it significantly: LLM and agent orchestration, MCP-style tool use, computer vision for shop-floor verification, video understanding for instruction generation, software interfaces for robots and humanoids on the factory floor, observability and deployment across multi-tenant SaaS, private cloud, and on-prem / air-gapped factories.
Strong experience in Java, .NET, Go, Python or similar is fine. Mindset matters more than prior framework experience. Learning a stack is easier than learning how to think about software at scale.
We are building the execution platform for hybrid manufacturing: the system that orchestrates work on the shop floor across humans, AI agents, and robotic systems.
The platform is used daily by thousands of operators across Europe to design, execute, and continuously improve work in high-mix, low-volume factories. The technical reality is rich: multi-level BOMs, variant-heavy product structures, sequence-dependent execution, version-controlled executable work.
And the bet on top is bigger: vision-based step verification, agentic execution supervision, autonomous continuous improvement, factory replay for model training. None of this is slideware. It is what we are building over the coming years.
