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Salary
≈ $39k – $104k per year (Estimated)
Location
In office (Budapest)
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 28, 2026. First seen by Alion on Aug 25, 2026. MP Solutions scores C on the Alion truth index.

Overview
Company
Impact
Profile match
MP Solutions is a Hungarian IT services company that provides SAP consulting, software development and engineering services to corporate clients. Its consultants work on SAP logistics, warehouse, production, transportation, maintenance and HR modules, while its engineering teams cover DevOps, machine learning and safety engineering. Most of its staff work in Budapest, with smaller teams in Pécs, Székesfehérvár and Nyírtelek. The company is headquartered in Budapest, Hungary.

Our client is a global leader in cybersecurity for IT, OT, and ICS critical infrastructure. Their end-to-end platform gives enterprises and public sector organizations the critical advantage they need to protect complex networks, secure devices, and meet compliance requirements.

Over the past 20 years, a consistent commitment to innovative technology has earned the trust of more than 1,700 organizations, governments, and institutions worldwide - cementing the company's role in protecting the world's critical infrastructure and securing our way of life.

On behalf of our client, we are looking for a driven and passionate Machine Learning Engineer who will own the data foundation the team runs on: dataset pipelines, labeling workflows, and data quality. Model quality is bottlenecked by data quality; this role removes that bottleneck. We've had a strong presence in Veszprém for over a decade, and we're now expanding into Budapest, this role is based in our newly opening Budapest office, right at the start of that growth.

You Will Have the Opportunity to

  • Train and fine-tune models against curated datasets; run systematic hyperparameter and architecture experiments
  • Build and maintain evaluation suites: regression tests for model behavior, benchmark tracking across model versions
  • Analyze model failures (false positives/negatives) and translate findings into dataset or training improvements
  • Package models with documentation, benchmarks, and reproducible evaluation results for Engine team handoff
  • Contribute to internal tooling for experiment tracking and model comparison

Requirements

  • 2+ years ML engineering or applied data science experience
  • Strong Python; hands-on experience with modern training and fine-tuning workflows
  • Experience with structured model evaluation (not just accuracy, precision/recall tradeoffs, drift, edge-case analysis)
  • Comfortable working against defined acceptance criteria and production constraints

Benefits

  • Stable, growing international company background with an exceptional customer group
  • Opportunity to improve your professional skills
  • The newest technology environment
  • Language course and opportunity for active recreation - kettlebell, football and office massage
  • Attractive working environment - nice office full of accessories (fruits every day, coffee, breakfast, tea etc.)
  • Regular team events and Happy Hour activities
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