Deep Learning Engineer (m/f/d)

Sportradar AG; Wien; Hybrid; EUR 60 000 - 90 000; Vollzeit

Zusammenfassung

A leading sports technology company is seeking a talented Deep Learning Engineer to join their Computer Vision Unit. You will design and optimize cutting-edge neural networks powering AI-driven sports solutions. This role offers the opportunity to innovate in a collaborative environment with a balance of autonomy and structure, contributing to impactful advancements in sports technology.

Leistungen

Flexible work hours

Global Employee Assistance Programme

Social events and teambuilding activities

Qualifikationen

• 4+ years experience developing and deploying deep learning models in production environments.
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Experience with computer vision libraries (OpenCV) and NLP libraries (Hugging Face Transformers).

Aufgaben

• Design and deploy state-of-the-art deep learning models for sports applications.
• Research novel neural network architectures to improve model performance.
• Build and maintain scalable data pipelines for training deep learning models.

Kenntnisse

Mathematics

Deep Learning

Computer Vision

NLP

Ausbildung

Master's degree in Computer Science or related field

Tools

Python

PyTorch

TensorFlow

OpenCV

Hugging Face Transformers

Docker

Kubernetes

Jobbeschreibung

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.

Job Description

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:

• Acollaborative environmentwith colleagues from all over the world (Engineering offices in Europe, Asia and US) including varioussocial eventsandteambuilding.
• Flexibilityto manage your workday and tasks withautonomy.
• A balance ofstructureandautonomyto tackle your daily tasks.
• Vibrantandinclusivecommunity, includingWomen in TechandPride groupswhich welcome all participants.
• GlobalEmployee Assistance Programme.
• CalmandReulay app(leading well-being apps designed to support focus, quality rest, mindfulness, and long-term mental resilience).

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