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 in London employer: Warner Music INC
As a leading innovator in the AI space, our company offers an exceptional work environment for ML Engineers and Data Scientists, where creativity meets cutting-edge technology. Located in a vibrant hub of innovation, we foster a collaborative culture that prioritises employee growth through continuous learning opportunities and hands-on experience with advanced AI techniques. Join us to be part of a mission-driven team that values your contributions and empowers you to make a significant impact in the world of predictive intelligence.
StudySmarter Expert Advice🤫
We think this is how you could land ML Engineer / Data Scientist, Applied AI in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those involving ML and AI. This is your chance to shine and demonstrate what you can bring to the table.
✨Tip Number 3
Prepare for interviews by practising common technical questions and case studies related to ML and data science. We want you to feel confident and ready to tackle any challenge thrown your way!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace ML Engineer / Data Scientist, Applied AI in London
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 that you’re not just skilled but also genuinely excited about the impact of AI in the music and entertainment industry.
Tailor Your Experience:Make sure to highlight your relevant experience in ML engineering or data science. We love seeing how you've taken models from concept to production, so share specific examples that showcase your skills and achievements in this area.
Keep It Clear and Concise:While we appreciate detail, clarity is key! Use straightforward language to explain your technical expertise and past projects. Remember, we want to understand your journey without getting lost in jargon.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Warner Music INC
✨Know Your Models Inside Out
Make sure you can discuss the machine learning models you've worked on in detail. Be ready to explain your approach to rapid modeling and experimentation, as well as how you validated your concepts. This shows you not only understand the theory but also how to apply it practically.
✨Showcase Your MLOps Knowledge
Since this role involves production engineering and MLOps, be prepared to talk about your experience with CI/CD practices and how you've implemented monitoring for deployed models. Highlight any specific tools or frameworks you've used to ensure reliable deployment.
✨Demonstrate Cross-Functional Collaboration
This position requires working closely with various teams, so share examples of how you've collaborated with product managers, data scientists, or business stakeholders in the past. Emphasise your ability to communicate complex ideas clearly to non-technical audiences.
✨Stay Curious About New Technologies
The job description mentions technology scouting, so show your enthusiasm for exploring new AI technologies. Discuss any recent advancements in generative AI or LLMs that excite you and how you see them being applied in real-world scenarios, especially in music and entertainment.
