At a Glance
- Tasks: Lead AI innovation by developing and scaling impactful machine learning models.
- Company: Join a forward-thinking company at the forefront of AI transformation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on creativity and innovation.
- Why this job: Make a real difference in AI while working with cutting-edge technologies.
- Qualifications: Bachelor's degree in a technical field and 2+ years of relevant experience.
The predicted salary is between 60000 - 80000 € per year.
This role is the technical engine of our AI transformation. You will be responsible for bringing our most impactful AI models out of the lab and scaling them into reliable, high-performance production systems.
Mission
Reporting to the VP Data Solutions & Innovation within the Business Intelligence organization, you will lead the technical effort in exploring, validating, and accelerating the next generation of AI use cases. Your mission is focused on rapid scientific discovery and robust engineering: you will design and execute advanced modeling experiments to unlock new business value, and you will ensure that the most successful prototypes are engineered into scalable, high-performance production systems. You will operate with an innovator's mindset, tackling complex, unstructured music and market data, using techniques such as Deep Learning and Generative AI. Your core objective is to maximize the rate of successful innovation and reliably deploy verified solutions, ensuring our entire BI ecosystem is propelled toward predictive and augmented intelligence.
Responsibilities
- Rapid Modeling & Experimentation: Design, develop, and benchmark state-of-the-art machine learning models (forecasting, segmentation, recommendation, NLP, etc.) with a strong emphasis on quick iteration and scientific validation of new concepts.
- Generative AI & Exploration: Lead hands‑on technical exploration into advanced techniques, including LLMs, RAG architectures, and Generative AI applications to create new forms of automated analysis and augmented intelligence products.
- Production Engineering & MLOps: Translate validated prototypes into robust, production‑ready specifications, and lead the implementation of MLOps best practices (CI/CD, monitoring, serving) required for the reliable deployment of models.
- Complex Data & Feature Engineering: Deeply explore complex, multi‑modal data (e.g., high‑dimensional data, text, time series) defining the necessary features and data pipelines to support highly accurate experimental models for strategic analysis.
- Cross-Functional Collaboration: Work closely with the Product Manager, Data Scientists, and business stakeholders to ensure technical solutions maximize tangible business impact and adhere to ethical AI standards.
- Technology Scouting: Drive innovation through hands‑on exploration of new AI technologies, including LLMs, GenAI, and vector databases, and evaluate their practical application to our music and operational data.
- Knowledge Transfer: Contribute to AI adoption and technical literacy across the company through clear documentation, workshops, and knowledge sharing with both technical and non‑technical teams.
Qualifications
- Education: Bachelor’s degree required in Applied Mathematics, Computer Science, Software Engineering, or a highly technical quantitative discipline. A Master’s degree (MS) or higher is strongly preferred.
- Experience: 2+ years of professional experience as a Machine Learning Engineer, Applied ML Scientist, or similar role, with a clear focus on productionizing models and advanced AI techniques.
- Technical Depth: Strong expertise in Python development and established skills in deploying and managing the full lifecycle of complex ML/DL models. Experience with advanced analysis of unstructured or multi‑modal data (e.g., high‑dimensional feature vectors, dense embeddings) is highly valued.
- MLOps Mindset: Proven track record of transforming R&D proofs‑of‑concept into robust, scalable, and monitored production‑grade ML solutions.
- Engineering Rigor: A background in software engineering best practices (clean code, testing, Git) is essential.
- Communication: Exceptional ability to communicate complex concepts and model limitations clearly and effectively to product and non‑technical stakeholders.
- Domain Affinity: High curiosity and enthusiasm for music, entertainment, or culture is a strong plus.
ML Engineer / Data Scientist, Applied AI employer: Warner Music INC
As a leading innovator in the AI space, we offer our ML Engineers and Data Scientists an exceptional work environment that fosters creativity and collaboration. Our commitment to employee growth is evident through continuous learning opportunities and hands-on exploration of cutting-edge technologies, all while being part of a vibrant culture that values diversity and ethical AI practices. Located in a dynamic city, we provide a unique chance to make a significant impact on the music and entertainment industry, ensuring your contributions are both meaningful and rewarding.
StudySmarter Expert Advice🤫
We think this is how you could land ML Engineer / Data Scientist, Applied AI
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those related to machine learning and AI. This is your chance to demonstrate your technical prowess and innovative mindset, so make it shine!
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past projects in detail. Remember, they want to see how you think and approach complex problems.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team and contributing to our AI transformation journey.
We think you need these skills to ace ML Engineer / Data Scientist, Applied AI
Some tips for your application 🫡
Show Your Passion for AI:When you're writing your application, let your enthusiasm for AI and machine learning shine through. We want to see how excited you are about the potential of these technologies and how you can contribute to our mission at StudySmarter.
Tailor Your Experience:Make sure to highlight your relevant experience in ML engineering or data science. We’re looking for specific examples of how you've taken models from concept to production, so don’t hold back on those details!
Keep It Clear and Concise:While we love a good story, clarity is key! Use straightforward language and avoid jargon where possible. We want to understand your skills and experiences without getting lost in technical terms.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to hear from you!
How to prepare for a job interview at Warner Music INC
✨Know Your Models Inside Out
Make sure you can discuss your experience with machine learning models in detail. Be prepared to explain how you've designed, developed, and benchmarked models, especially in areas like forecasting and NLP. This shows you’re not just familiar with the concepts but have hands-on experience.
✨Showcase Your MLOps Knowledge
Since this role involves production engineering and MLOps, be ready to talk about your experience with CI/CD practices and how you've implemented monitoring for ML models. Highlight any specific tools or frameworks you've used to ensure reliable deployment.
✨Demonstrate Cross-Functional Collaboration
Prepare examples of how you've worked with product managers, data scientists, and other stakeholders. Discuss how you ensured that technical solutions aligned with business goals and adhered to ethical AI standards. This will show your ability to communicate complex ideas effectively.
✨Stay Curious About New Technologies
Express your enthusiasm for exploring new AI technologies like LLMs and Generative AI. Share any recent projects or research where you’ve applied these techniques. This demonstrates your innovator's mindset and commitment to driving AI transformation.
