Machine Learning Engineer (ongoing)

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.

Build end-to-end machine learning solutions including data pipelines, model training, deployment, and monitoring. Translate business questions into technical ML problem definitions and ensure systems are reliable in production environments.

Requires strong engineering instincts, cloud fluency, and proficiency in Python and ML foundations. Candidates should have experience with data platforms, MLOps basics, and the ability to ship models into real-world systems.

• Equity

• Microsoft AzurePreferred
• SQLPreferred
• CI/CDPreferred
• Machine learningPreferred
• Amazon Web ServicesPreferred
• Google CloudPreferred
• PythonPreferred

Identified from the job description. Confirm important requirements above.

• Python
• Machine Learning
• SQL
• AWS
• GCP
• Azure
• MLOps
• CI/CD
• Data Pipelines
• Model Deployment
• Model Monitoring
• Feature Engineering
• Cloud Computing
• API Integration
• Containerization

• Data & Analytics
• Software
• Technology
• Engineering
• Consulting

Minimum experience

2+ years

Apply by

Aug 25, 2026

Posting language

English

Working hours

40 hours per week

Seniority

Entry level