Senior Machine Learning Engineer

What You'll Be Doing

• Implement and Deploy ML Models: Implement, and deploy machine learning models, utilizing a variety of modeling approaches to address different business problems.
• Advanced ML Techniques: Implement systems with Generative AI (GenAI) and emerging architectures like Retrieval-Augmented Generation (RAG).
• Scalable Training Solutions: Scale training processes across multiple GPUs, leveraging state-of-the-art frameworks such as NVIDIA's cuDNN and TensorRT.
• Support AI and ML Pipelines: Assist various teams in deploying AI and ML pipelines on Google Cloud using Vertex AI, ensuring robust and scalable solutions.
• Automated ML Pipelines: Design and implement automated machine learning pipelines, including fully automated training and prediction processes.
• MLOps Best Practices: Apply MLOps best practices for model deployment, monitoring, and automation, enhancing the efficiency and reliability of ML operations.
• Containerization: Work with containerization technologies to streamline the deployment and management of ML models.
• Infrastructure-as-Code: Implement infrastructure-as-code using Terraform to ensure reproducible and scalable ML environments.
• Innovation and Problem-Solving: Apply strong problem-solving skills and creative thinking to champion the adoption of AI technologies that address business needs.
• Collaboration and Communication: Collaborate effectively with cross-functional teams, demonstrating excellent communication and teamwork skills in a fast-paced environment.

What You Bring

• Bachelor's or master’s degree in Computer Science, Engineering, Mathematics, or a related field.
• Strong proficiency in Python and experience with deep learning frameworks, particularly PyTorch.
• In-depth understanding of various modeling approaches, Generative AI, and emerging architectures like Retrieval-Augmented Generation.
• Solid understanding of cloud computing concepts and experience with cloud platforms, preferably Google Cloud Platform (GCP).
• Familiarity with MLOps best practices and tools for model deployment, monitoring, and automation.
• Experience working with containerization technologies and container registries.
• Proficiency in designing and implementing automated machine learning pipelines.
• Experience with infrastructure-as-code, preferably Terraform.
• Strong problem-solving skills and the ability to think creatively to develop innovative solutions.
• Excellent communication and teamwork skills, with the ability to collaborate effectively in a fast-paced environment.

It's also a plus if you have...

• Hands-on experience with scaling training processes across multiple GPUs, using frameworks like NVIDIA cuDNN and TensorRT.
• A understanding of cloud security practices and data compliance protocols.
• Experience in leading initiatives for academic collaboration and community engagement.

What We Offer

• A modern office in the heart of Berlin and flexible options for remote work.
• Numerous benefits such as a free BVG job ticket, a reduced-price membership for Urban Sports, discounts for Bertelsmann employees etc.
• Ongoing training opportunities on our internal platforms BMG Campus and Bertelsmann University.
• Long-term career opportunities for all employees.
• A collaborative work environment with an open, appreciative community of people who all share the same passion for working with artists and songwriters.
• Our unique corporate culture based on strong values and mutual support that make us a great team.