Machine Learning Engineer - Allegro Pay

Quick Facts

Machine Learning Engineer

Description

As a Machine Learning Engineer, you will be responsible for the machine learning/data platform and support Data Scientists in building machine learning models, enabling their shipping to production, and ensuring their high availability and performance.

Responsibilities

• Develop a modern MLOps ecosystem aimed at automating the process of building and deploying machine learning models
• Design and implement a modern platform for training predictive models (classifiers, deep neural networks, graph models)
• Deploy models to production and optimize deployment pipelines to enable Data Scientists’ self-service
• Manage, monitor, and recalibrate models deployed to production
• Support work on a Feature Store providing predictors for models operating in production
• Build connectors with a FinTech AI platform to enable agentic workflows interacting with models and features

Requirements

• Degree in Computer Science, Mathematics, or another technical major
• At least 2 years of experience building ML-driven solutions
• Fluency in Python; know MLE toolchain libraries (scikit-learn, PyTorch, Pandas, FastAPI/Flask)
• Comfortable with AI assisted programming tools (Opencode, Codex, Claude)
• Experience with modern Python-based orchestration tools (Airflow, Dagster)
• Good analytical skills and knowledge of SQL
• Know and apply DevOps principles
• Practical understanding of statistical and machine learning methods (decision trees, neural networks)
• Ability to make independent decisions and take ownership of created code
• Nice-to-have: Experience with the .NET ecosystem
• Nice-to-have: Knowledge of cloud-based MLOps tools (AzureML, Google Vertex AI, AWS Sagemaker)
• Nice-to-have: Knowledge of dbt

Benefits

• Flexible working hours in a hybrid model (4/1), with 30 days of occasional remote work
• Annual bonus based on performance and company results (up to 10% of gross annual salary)
• Well-located offices and excellent work tools
• Cafeteria plan fringe benefits (e.g., medical, sports, lunch packages, insurance, purchase vouchers)
• English classes paid for related to the job
• Training budget, internal learning platform, and other learning activities
• Additional day off for volunteering
• Social events