Senior Machine Learning Engineer, Apheris

• 7+ years of relevant working experience, or a Bachelor / Master / PhD in Computer Science or related fields (e.g., Physics, Mathematics, Engineering) and 5+ years of relevant working experience
• Expert understanding of putting ML and AI into practice. Hands-on experience in production-grade, large-scale ML model development and operationalization
• Real industry experience in operationalization of ML, from using open source frameworks (e.g., MLflow, Kubeflow) to managed services / cloud provider offerings (e.g. AWS Sagemaker, Google AI Platform, Azure Machine Learning, Databricks, DataRobot, …) to specific on-prem solutions (e.g., Dkube)
• Deep understanding of the full lifecycle of an ML solution and how it can fit in a business process to make real impact
• Deep expertise of how different tools in the ML/MLOps space can be leveraged together to build a best-in-class ML infrastructure, and the market/tooling trends related to that
• Strong experience in Python coding, used both for ML and automation tasks. Further programming languages are a plus (e.g., R, Go, Rust, JavaScript)
• Experience with cloud technologies and providers (preferably AWS) with a focus on security, scalability and maintainability
• Experience in developing enterprise-grade ML-enabled products
• Passionate about continuous learning, demonstrated agility in quickly adopting and mastering new technologies
• Excellent communicator: you are fluent in English (verbal and written) and communicate in a constructive and proactive manner
• You are confident to take ownership of a problem, gather ideas to solve it and convey your approach efficiently
• You have genuine interest in working with cutting-edge technology in a fast-paced environment and a young start-up

Desirable

• ​As a Senior Machine Learning Engineer at Apheris, you will build our Federated Machine Learning and Analytics Platform – a platform to help fellow Machine Learning (ML) engineers like you to make running Federated Machine Learning easy, secure and governable
• You will be in charge of discovering, designing and implementing components to the platform to manage MLmodels throughout their full lifecycle
• With our team of best-in-class software engineers and data scientists, you will be working on challenging deep tech topics and design a product that serves the needs of our high-profile customers from various industries
• Leveraging your expertise, you will be co-responsible for meeting our product goals. We empower you to be a major contributor to the success of our product and thereby strongly influence how organizations securely collaborate on data science and AI across boundaries
• In the role of Senior ML Engineer, you will genuinely impact the way the world securely collaborates on AI and interacts with data
• Build and implement ML infrastructure that allows our users to launch large scale models on distributed data
• Design and develop our product to work well with state-of-the art ML tooling and platforms by leveraging your experience with these
• Deeply understand our users (ML Engineers) to design a scalable product such that it empowers their full end-to-end lifecycle on our federated ML Platform
• Determine which tools and platforms we need to integrate or interface with to help our users train large scale ML models via a federated infrastructure
• Bring world-class experience and expertise in MLOps technology, concepts and tools to our team
• Research and implement/integrate ML and MLOps tools, frameworks and platforms to enhance our Apheris Platform
• Work as part of our agile software development team designing and implementing the Apheris Product

• Our interview process is split into three phases:
• Initial Screening: If your application matches our requirements, we invite you to an initial video call to explore the fit. In this 30-45 minutes interview, you will get to know us and the role
• The interviewer will be interested in your relevant experiences and skills, as well as answer any question on the company and the role itself that you may have
• Deep Dive: In this phase, a domain expert from our team will assess your skills and knowledge required for the role by asking you about meaningful experiences or your solutions for specific scenarios in line with the role we are staffing
• Final Interview: Finally, we invite you for up to three hours of targeted sessions with our founders, talking about our culture and meeting future co-workers on the ground