Senior Machine Learning Engineer

You are:

A Senior Machine Learning Engineer passionate about building and deploying data-driven products that power real business impact. You bring deep expertise in machine learning and software engineering, and you're eager to work alongside data scientists, MLOps engineers, and stakeholders across the business to solve complex problems at scale. You enjoy designing ML systems that are not only predictive but production-ready, scalable, and maintainable. You thrive in fast-paced environments, are detail-oriented, and are driven by delivering measurable results through applied ML.

You will:

• Design and build production-grade machine learning systems that support personalization, user engagement, fraud detection, or customer lifetime value modeling.

• Own the entire ML lifecycle from prototyping to deployment, working closely with MLOps and data engineering to ensure robust and scalable infrastructure.

• Apply advanced modeling techniques (regression, classification, time series forecasting, etc.) using structured and unstructured data.

• Develop and maintain robust data and feature pipelines, supporting real-time and batch inference.

• Benchmark model performance and lead experiments to optimize predictive power, fairness, and business value.

• Document your work clearly and concisely to ensure reproducibility and facilitate collaboration across teams.

• Leverage tools like MLflow to manage experiments, model versioning, and deployment.

• Partner with stakeholders across product, engineering, and analytics to translate business needs into machine learning solutions.

• Actively contribute to the technical excellence of the team by reviewing code, sharing knowledge, and promoting best practices.

Requirements

• A B.S. or M.S. in computer science, statistics, data science, engineering, mathematics, or a related field.

• 4+ years of experience working in applied machine learning or data science roles, with a strong focus on production deployment.

• Proficiency in Python and core ML libraries (Scikit-learn, XGBoost, PyTorch/TensorFlow, etc.).

• Strong SQL skills and comfort working with data warehouses like AWS Redshift, and solutions like PostgreSQL, MS Server, etc.

• Experience building and maintaining robust data pipelines and features for ML workflows.

• Solid understanding of supervised and unsupervised ML algorithms, model evaluation techniques, and common pitfalls.

• Familiarity with MLflow or similar frameworks for experiment tracking and model lifecycle management.

• Comfort with Git-based workflows and CI/CD integration for ML.

• Strong collaboration and communication skills in English.

• Curiosity, humility, and a drive to continuously improve yourself and the products you build.

Nice to have

• Experience with AWS services such as S3, Lambda, SageMaker, ECR, and CloudWatch.

• Exposure to tools in our tech stack: MLflow, Airflow, dbt, Tableau, GitHub, PostgreSQL.

• Understanding of MLOps best practices (monitoring, drift detection, retraining pipelines).

• Previous experience in the iGaming, sports betting, or digital media industries.

Benefits

• Private health and life insurance

• English lessons with a Native Speaker

• Psychological support

• MultiSport card

• Lunch card - for employees who work in the office

• Flexible working hours

  
  
  Note: Flexible working hours and occasional work from home options in Better Collective help us achieve a proper work-life balance. We strongly believe in the magic of teamwork, though, so we come to the office at least three days a week to keep the team spirits high.

Application Deadline

We look forward to hearing from you and accept applications until the 5th of July
Please submit your CV and cover letter in PDF; only applications submitted in English will be considered.

Expected start date: as soon as possible.