Deep Learning Engineer (m/f/d)

We’re the world’s leading sports technology company, at the intersection between sports, media, and betting. More than 1,700 sports federations, media outlets, betting operators, and consumer platforms across 120 countries rely on our know-how and technology to boost their business.

ABOUT US:

We are Computer Vision Unit - enhancing the sports experience for fans, athletes, and teams across the globe through world-class AI solutions and are committed to advancing the state of the art of Sport through continuous innovation with a specialized and distributed team focused on research and development in areas such as computer vision, machine learning, deep learning, data science and beyond. We are on a mission to automate sports content creation - the data and video – that feeds products and services we deliver to our partners. The possibilities to revolutionize the world of sport are vast, and we are only at the beginning of our journey.

We are looking for a talented Deep Learning Engineer to join our team to help us improve our products at the forefront of technology.

THE CHALLENGE:

As a Deep Learning Engineer, you will be at the forefront of designing, implementing, and optimizing cutting-edge neural network architectures that power our AI-driven sports solutions. You will collaborate with cross-functional teams to translate complex business problems into innovative deep learning models.

Responsibilities

• Design, develop, and deploy state-of-the-art deep learning models for various sports applications including computer vision, natural language processing, and time-series analysis.
• Research and implement novel neural network architectures and training methodologies to improve model performance and efficiency.
• Build and maintain scalable data pipelines for training, validating, and deploying deep learning models.
• Optimize models for inference across different hardware platforms.
• Integrate models into production systems.
• Stay current with the latest advancements in deep learning research and implement relevant techniques.
• Contribute to technical documentation and knowledge sharing within the team.

ABOUT YOU:

• Strong mathematical foundation in linear algebra, probability, and statistics.
• 4+ years of practical experience developing and deploying deep learning models in production environments.
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Experience with model optimization techniques including quantization, pruning, and knowledge distillation.
• Hands-on experience with computer vision libraries (OpenCV, TorchVision) and NLP libraries (Hugging Face Transformers).
• Knowledge of MLOps tools and practices (MLflow, W&B, DVC, ClearML).
• Experience with containerization (Docker) and orchestration (Kubernetes) technologies.
• Familiarity with cloud platforms (AWS, GCP, Azure) and their ML services.
• Understanding of model serving frameworks (TorchScript, TensorRT, ONNX, TorchServe).
• Experience with modern deep learning architectures (Transformers, GANs, Diffusion Models).
• Ability to balance research exploration with practical implementation and delivery.

OUR OFFER:

• A collaborative environment with colleagues from all over the world (Engineering offices in Europe, Asia and US) including various social events and teambuilding. 
• Flexibility to manage your workday and tasks with autonomy. 
• A balance of structure and autonomy to tackle your daily tasks. 
• Vibrant and inclusive community, including Women in Tech and Pride groups which welcome all participants. 
• Global Employee Assistance Programme. 
• Calm and Reulay app (leading well-being apps designed to support focus, quality rest, mindfulness, and long-term mental resilience). 
• Online training videos. 
• Flexible working hours. 

While we appreciate the flexibility and benefits of working from home, we strongly believe that coming together in person fosters stronger connections, encourages collaboration, and drives innovation—both as individuals and as a company. The energy, shared ideas, and team support we experience in the office strengthen the foundation of our success and culture. For this reason, we are an office-first business operating on a hybrid model, with team members working in the office three days a week to build relationships, exchange ideas, and grow together.

OUR RECRUITMENT PROCESS:

• Initial Screening: A quick chat with our Talent Acquisition Partner to understand your background and expectations.
• ·First (Technical) Interview: A live coding exercise with the Hiring manager and one of the Team Leads. Covering mostly your technical skills and knowledge, as well as discussion about your overall technical experience.
• Second (Soft-skills) Interview: Interview with Tribe Technical Owner and (Tribe) Product Owner, to discuss about your soft skills and team/cultural fit. 
• Final Steps: Receive feedback and, if successful, an offer!

We keep it simple and aim to wrap up the process within 3 weeks

At Sportradar, we celebrate our diverse group of hardworking employees. Sportradar is committed to ensuring equal access to its programs, facilities, and employment opportunities. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. We encourage you to apply even if you only meet most of the requirements (but not 100% of the listed criteria) – we believe skills evolve over time. If you’re willing to learn and grow with us, we invite you to join our team!