Staff Machine Learning Engineer (Credit International ML)

Role overview

Provide technical leadership for machine learning systems that inform credit lending decisions across international markets. You’ll guide the strategy and development of models for predicting repayment behavior, while partnering with product, engineering, risk, and ML platform leaders to build capabilities that can scale. The role combines hands-on engineering with team mentorship and long-range technical planning.

Responsibilities - Set the team’s technical direction over a year-long horizon and connect it to high-impact projects. - Partner with ML platform, product, engineering, and risk leaders on modeling capabilities for international expansion. - Define and advocate for technical solutions and operating practices that help the team deliver effectively. - Establish strong code review and design standards to support quality and ownership. - Mentor engineers through feedback, guidance, and hands-on example.

Requirements - At least 8 years of experience researching, designing, deploying, and operating large-scale, real-time ML systems; a relevant PhD may count for up to 2 years. - Strong Python skills and experience writing production-quality software. - Experience building and evaluating tabular classification models, including gradient-boosted trees or deep learning approaches. - Experience with distributed data processing or parallel compute frameworks such as Spark or Ray. - Familiarity with ML lifecycle tools for training orchestration, experimentation, and model monitoring. - Strong engineering judgment, hands-on technical leadership, and clear written and verbal communication.

Nice to have - Proficiency with AI-assisted development tools for accelerating model development. - Experience with LightGBM, XGBoost, PyTorch, Kubeflow, Airflow, or MLflow.

Benefits and work setup

The role is remote within Poland. The package includes base pay, equity eligibility, private healthcare, wellness and technology stipends, paid leave, learning support, and other wellbeing resources. Compensation and benefits depend on role-related factors and applicable policies.