Apply📍 Spain
🔍 HealthTech and AI
- 3-4 years of experience in a similar ML platform engineering role, ideally with production model deployment experience.
- Strong passion for building robust and scalable ML platforms.
- Solid understanding of optimization techniques, multithreading, and distributed system concepts.
- Foundation in computer science principles, including data structures, algorithms, and complexity analysis.
- Experience building and maintaining software systems, preferably in a cloud environment (e.g., AWS, GCP, Azure).
- Experience managing GPU resources, including driver management, access control, allocation, and memory management (NVidia, CUDA).
- Familiarity with machine learning frameworks such as TensorFlow or PyTorch.
- Experience with experiment tracking and model management tools (e.g., MLflow, TensorBoard).
- Experience with containerization technologies (Docker, Kubernetes) and version control systems (e.g., GitHub).
- Excellent problem-solving, communication, and collaboration skills.
- Ability to work independently and as part of a team.
- Comfortable with CI/CD practices, code reviews, and collaborative development.
- Design, develop, and maintain tools and infrastructure for ML model training, experimentation, and deployment.
- Develop systems for efficient access to and management of large datasets.
- Create solutions for optimizing GPU utilization and resource allocation.
- Integrate and maintain experiment tracking and monitoring tools (e.g., MLflow, TensorBoard).
- Develop processes for deploying ML models to production environments.
- Collaborate closely with ML engineers to understand their needs and provide effective solutions.
- Contribute to improving ML development lifecycle and best practices.
- Troubleshoot and resolve ML platform-related issues.
- Stay current with advancements in ML platform technologies and best practices.
AWSDockerPythonGCPGitKubernetesMachine LearningMLFlowPyTorchAlgorithmsAzureData StructuresTensorflowCI/CD
Posted 3 months ago
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