We are looking for an ML Ops Engineer

We are looking for an ML Ops Engineer to own the platform our ML team trains, evaluates and ships models on. Our models learn from engineering drawings and CAD, and they train on GPUs in our office and in the cloud. You'll make that platform fast, reproducible and secure, so the ML team can spend its time on models instead of machines. This is a high-agency role: the foundations are in place, and you'll decide how they grow.

Apply here

Location: Stockholm, Sweden
Employment type: Full-time
Department: Engineering

We are Encube

At Encube, we’re building the agentic platform where hardware teams come together to explore designs, share insights and shape ideas to invent better products faster where 3D is at the center.

Every hardware product is described twice: in the 3D model, and in the 2D drawings that go to the shop floor. We're building ML that works across both. It understands how a drawing relates to its model, and it generates CAD and drawings from what an engineer needs.

What you’ll do

Own our training infrastructure across on-prem and cloud GPUs.
Make every experiment reproducible: versioned datasets, tracked runs, and models you can trace back to the data they were trained on.
Run the infrastructure behind our generative CAD agents and their evaluations.
Build stateful GPU sessions: interactive compute that pauses when idle, keeps its state, and resumes in seconds on any machine.
Keep GPU costs in check and customer data secure.
Work side by side with ML engineers and remove whatever slows them down.

Our tech stack

PyTorch and CUDA, SkyPilot and MLflow, on AWS. It's early days, and you'll have real impact on the tools we choose.

What we’re looking for

Experience building and running ML infrastructure that other engineers depend on
Hands-on with GPUs: CUDA, drivers, cloud capacity and cost
Infrastructure as code and solid cloud fundamentals
Care for reproducibility and data security
Bonus: you've trained models yourself, or run LLM agents in production

Why this role

The workloads are unusual. 3D geometry, rendering and model training.
Your users sit next to you. The ML engineers on your platform are the people at the next desk.

How we work

Autonomy and trust. High ownership, high agency, minimal bureaucracy. You'll have real agency from day one.
Close to customers. The engineers using Encube send us real drawings and real parts, so you always know whether what you built matters.
Truth-seeking, humble team. We value learning, experimentation and impact over ego. We write things down, propose before we build, and measure before we claim.
High standards. Everyone on the team pulls their own weight.

We look for engineers who think in products, not just systems: people who take ownership, drive clarity when it's missing, and create lift rather than drag.

Sound like you?

Apply below along with your CV, tell us in a few sentences about one system you built: what it did, how you knew it worked, and what you'd do differently.

Apply here

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