About the Team
At Albatross, we are on a mission to revolutionize user engagement. Through advanced AI-driven personalization, we generate inspiring user experiences that increase conversions, and loyalty. Our founders are recognized thought-leaders with a proven track record of delivering personalization solutions at leading tech companies. We are at an exciting stage, having raised significant funding to develop the most advanced recommendation platform. We obsess over our customers and value excellence, ownership, speed, and above all, delivering results. Being part of Albatross means continuously learning, innovating, shaping our products, and directly impacting users. Come and join us!
About the Role
As a Principal Machine Learning Engineer at Albatross, you will take ownership of the engineering aspects of our AI engines. You will be responsible for scaling the training of our deep learning models across vast datasets, managing the efficient access and utilization of massive amounts of data, and deploying real-time prediction services with minimal latency. Additionally, you will handle large embedding spaces at scale, ensuring cost-effective and low-latency operations. Your role is critical in ensuring that our AI infrastructure is robust, scalable, and capable of delivering high-performance recommendations.
Based in Berlin, this position operates under a hybrid work model, with three days in the office each week. We also provide relocation assistance for new team members. You will report directly to our Chief Scientist and work closely with our CTO and the rest of the technical team to drive our AI strategy and execution.
In This Role, You Will
- Scale Model Training: Develop and implement scalable solutions for training deep learning models on large datasets, ensuring efficient resource usage and performance.
- Data Management: Design and manage the infrastructure for accessing and processing vast amounts of training data, optimizing for speed and cost-efficiency.
- Real-Time Predictions: Build and deploy systems to serve at scale real-time predictions with minimal latency, integrating seamlessly into production environments.
- Embedding Space Management: Handle large embedding spaces, optimizing for both scale and performance to support our AI models.
- ML Operations: Implement and manage ML operations (MLOps) practices, including the use of containerization (Docker) and orchestration (Kubernetes) for model deployment and scaling.
- Performance Optimization: Continuously monitor and optimize the performance of our AI systems, identifying and addressing bottlenecks and inefficiencies.
- Collaborative Development: Work closely with applied scientists, engineers, and product teams to align technical solutions with business needs and ensure seamless integration and deployment of AI capabilities.
- Code Quality and Maintenance: Maintain high standards of code quality, ensuring robust, maintainable, and scalable engineering solutions.
- Infrastructure Management: Manage cloud-based and on-premise infrastructure to support scalable and reliable AI systems.
Qualifications
- Experience: Minimum of 5 years in machine learning engineering or related roles, with a focus on deploying and scaling ML models in production environments.
- Education: Master’s degree in Computer Science, Engineering, or a related field. PhD is a plus but not required.
- Programming Skills: Proficient in Python and other relevant programming languages. Experience with deep learning frameworks and libraries such as TensorFlow, PyTorch, and scikit-learn.
- Scalable Systems: Proven experience in building and managing large-scale distributed systems and data pipelines.
- MLOps Expertise: Strong knowledge of MLOps practices, including containerization (Docker), orchestration (Kubernetes), and continuous integration/continuous deployment (CI/CD) tools and processes.
- Real-Time Systems: Experience with designing and deploying real-time prediction services and optimizing for low latency.
- Data Management: Deep understanding of data storage and management strategies for handling large volumes of data efficiently.
- Cloud Platforms: Experience with cloud computing platforms (AWS, GCP, Azure) and managing AI/ML infrastructure in the cloud.
- Problem-Solving Skills: Strong analytical and problem-solving abilities, with a focus on optimizing performance and scalability.
- Collaboration and Communication: Excellent communication skills, with the ability to work effectively in a collaborative team environment and translate complex technical concepts into actionable strategies.
Why Albatross?
- Innovative Work: Engage in groundbreaking AI projects that transform user experiences and drive industry advancements in personalization technology.
- High-Impact Role: Your work will directly influence the scalability and efficiency of our AI systems, making a tangible impact on our business and our users.
- Dynamic Team: Join a team that values expertise and innovation, providing opportunities for continuous learning and professional growth.
If you are a seasoned machine learning engineer passionate about building scalable, high-performance AI systems, we invite you to join our team and help drive the future of personalization at Albatross!
