[Hiring] Machine Learning Engineer @Sofascore

Role Description

At Sofascore, we build products used by tens of millions of people worldwide, turning large volumes of sports and user data into meaningful experiences. We are looking for a Machine Learning Engineer to join our AI Team and work on real-world ML problems across:

• Recommender systems and personalized feeds
• Semantic search
• Sentiment analysis
• NLP and LLM-powered applications
• Other machine learning use cases, potentially including computer vision

This is an engineering-focused ML role. We are looking for someone who can take an ambiguous product or business problem, understand the data behind it, choose an appropriate approach, and turn it into a well-engineered ML solution.

You will work on both new and existing ML systems and collaborate closely with engineers, analysts, and product teams. At Sofascore's scale, technical decisions have real consequences: scalability, latency, reliability, computational cost, and maintainability all matter alongside model quality.

Your Responsibilities

• Design, develop, evaluate, and improve machine learning solutions for real product problems used by tens of millions of users worldwide
• Work on a broad range of ML use cases, including recommender systems, personalized feeds, semantic search, sentiment analysis, NLP, LLM-powered applications, classical machine learning problems, and potentially computer vision
• Take ownership of ML problems end-to-end: from understanding the business problem and exploring the data to selecting an approach, building and evaluating models, and collaborating on their integration into our systems
• Develop new ML solutions while also improving and maintaining existing systems
• Build LLM-powered solutions and contribute to the design of retrieval-augmented generation and other modern NLP systems
• Write clean, maintainable, testable, production-quality Python code following sound software engineering practices
• Work with large datasets using Python and SQL, and build reliable data and model workflows
• Evaluate models rigorously, select meaningful metrics, identify issues such as overfitting and data leakage, and understand the trade-offs behind different modelling approaches
• Collaborate with product managers, analysts, software engineers, and other stakeholders to translate product and business needs into well-defined ML problems
• Critically evaluate proposed solutions and choose the right level of complexity for the problem
• Stay current with developments in machine learning and LLMs, applying new techniques where they provide meaningful value

Qualifications

• 2+ years of professional industry experience in a Machine Learning or closely related engineering role
• A Master's degree in Computer Science, Software Engineering, Mathematics, Electrical Engineering, Data Science, or another relevant technical or quantitative field
• Strong foundations in machine learning, including supervised and unsupervised learning, model selection, feature engineering, regularization, validation, hyperparameter optimization, and appropriate evaluation methodologies
• Strong understanding of modern deep learning methods and architectures, including transformers
• Hands-on professional experience working with LLMs, including understanding transformer architecture, attention, tokenization, context windows, inference, and fine-tuning approaches
• Understanding of RAG systems and key concepts such as embeddings, retrieval, and evaluation
• Strong Python programming skills and the ability to write clean, maintainable, testable, production-quality code
• Hands-on experience with modern ML frameworks and libraries such as PyTorch, scikit-learn, TensorFlow, or similar
• Good practical knowledge of SQL and experience independently working with large datasets
• Practical experience with Docker and containerized development
• Strong analytical and critical-thinking skills
• Ability to work independently on ML problems, make sound technical decisions, and take ownership from problem definition through implementation and evaluation
• Strong communication and collaboration skills

Requirements

• Experience building recommender systems, ranking systems, or personalization solutions
• Practical experience designing and implementing RAG systems, including advanced retrieval, reranking, or evaluation approaches
• Experience fine-tuning LLMs, working with open-source language models, optimizing inference, or serving models at scale
• Experience taking ML systems into production and working across the broader ML lifecycle, including training, experiment tracking, deployment, CI/CD, model serving, monitoring, and retraining
• Experience with Kubernetes and container orchestration
• Experience designing or operating large-scale, consumer-facing ML systems
• Experience with cloud platforms and cloud-based ML infrastructure
• Experience with A/B testing or measuring the impact of ML models on real product and business metrics
• Experience with modern NLP methods beyond LLM applications or with computer vision
• Experience mentoring other engineers, sharing technical knowledge, or contributing to engineering standards
• Research or applied innovation experience

Benefits

• The opportunity to work with a cutting-edge sports platform impacting millions worldwide
• Family benefits package
• Education - internal and through international conferences and workshops
• Top-quality equipment and budget for a mobile phone
• Paid package of general physical examination once a year
• Sofascore Canteen (lunch options)
• Sofascore Bar (coffee and drinks on us)
• Numerous other benefits that we would verbally communicate to you