What if your next ML role started with messy data, cloud constraints, and a system that has to work in production (not just in a notebook)?

At Brillian, that’s often where our work begins. We help companies figure out what’s actually worth building with machine learning, why it matters, and how to make it real in practice.

We’re continuously looking to meet Machine Learning Engineers who enjoy building end-to-end solutions: data → features → models → deployment → monitoring. We don’t always have a matching project starting immediately, but when the fit is right, we’ll stay in close contact and move quickly when something clicks!

What our projects typically look like

You’ll work on greenfield and scaling-phase ML projects that create real business impact. Often, the biggest wins come from getting the data and cloud foundations right, so you’ll be close to those parts too.

Depending on the client and project stage, you might be:

Turning business questions into ML-ready problem definitions and success metrics
Building and improving data pipelines (ingestion, transformation, validation)
Working with modern data platforms to make data usable for modeling and analytics
Designing feature pipelines and training/evaluation workflows that are reproducible
Training, evaluating, and iterating models with clear experiment tracking
Deploying models (batch and/or real-time) and integrating them into products and workflows
Setting up monitoring for data quality, drift, performance, reliability, and cost
Collaborating with engineers and stakeholders to keep delivery grounded in real value

What we value (and what usually works well here)

You don’t need to match a perfect checklist. We care about how you think, how you build, and how you make ML useful in real environments.

Strong engineering instincts and a bias for maintainable solutions
Data realism: you understand pipelines and data quality are part of the ML solution
Production mindset: you care about reliability, monitoring, and running systems over time
Cloud fluency: you can build and troubleshoot in modern cloud environments
Pragmatism: you can say “this shouldn’t be ML” when that’s the right call
Collaboration and clear communication across technical and non-technical teams

And yes, practically speaking, experience in these areas helps a lot:

Python + ML foundations (modeling, evaluation, experimentation, feature thinking)
Data platforms and pipelines (SQL, transformations, orchestration, data quality checks)
Cloud (AWS, GCP, or Azure) and building in cloud-native ways
Shipping ML (APIs or batch jobs, containers, integrations into real systems)
MLOps basics (reproducibility, CI/CD for ML, model registry, monitoring)

Bonus (nice to have): software generalist skills. Many projects include small “make it usable” tasks alongside ML: light UI work, internal tools, dashboards, or wiring outputs into user-facing workflows.

What we offer

High-ownership work where you’ll help shape the solution, not just implement a spec
A team that values clarity and quality (and knows when “simple” beats “fancy”)
Hybrid setup from Helsinki or Tampere, with flexibility to focus when it matters
Salary typically €5,000–€7,000 per month, depending on experience and impact
Opportunity for equity for all new Brillians

What next?

Not sure if you tick every box? That’s okay. We value strong thinking, solid engineering, and the ability to make ML useful in real environments more than buzzword coverage.

Apply via the link below so we can process your application properly. If the timing isn’t perfect right now, we’re still happy to start the conversation and keep in touch.

Please note: we currently hire only within Finland and cannot offer visa sponsorship.

Brillian

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