Machine Learning Engineer

ABOUT SOFASCORE

Sofascore is a sports-tech company created with one goal in mind – giving sports enthusiasts a deeper understanding of the game. 

Our platform is the leading provider of advanced sports insights. From the biggest derbies to amateur matches, every game counts - that’s why we have the largest data coverage with 20,000+ tournaments across 25 sports. This comes easy with Torneo, our very own tournament management software for lower leagues.

The global recognition of the Sofascore Rating, along with the Player of the Season award for the highest-rated players, positioned us as the authority in evaluating player performance.

The Sofascore team counts more than 300 experts in 20 teams, primarily playing at our home court in Croatia, but we also have talents showing their skills worldwide.

More about the company /// More about the platform

ABOUT THE ROLE

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, LLM-powered applications, and other machine learning use cases, potentially including computer vision.

This is an engineering-focused ML role. We are looking for someone who goes beyond experimenting with models in notebooks: 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 do not need to have worked on every type of problem we solve. What matters is a strong foundation in machine learning and software engineering, hands-on experience with modern ML and LLM systems, and the ability to think critically about which approach is appropriate for a given problem.

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, whether that means a simple heuristic, classical ML, deep learning, a recommender system, or an LLM-based approach

• Stay current with developments in machine learning and LLMs, while applying new techniques where they provide meaningful value rather than complexity for its own sake

What you bring to the team

• 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, combined with a solid understanding of how they work beyond the API level, including 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: you should be comfortable challenging assumptions, identifying limitations in data or proposed approaches, and explaining why a particular solution is appropriate

• 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 and the ability to work effectively across engineering, analytics, and product teams

What sets you apart

You do not need to have all of the following. These are additional strengths that would make your experience particularly relevant to the problems we work on:

• Experience building recommender systems, ranking systems, or personalization solutions

• Practical experience designing and implementing RAG systems, including more 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, particularly where scalability, latency, reliability, and computational cost are important engineering considerations

• 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

What we offer 

• 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

Sounds good? It gets even better!

Send us your CV in English.

Looking forward to hearing from you. Let’s get the ball rolling! 🔥