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Senior ML Infrastructure Engineer
HíbridoTiempo completoAI EngineeringBiotechnology
Build and evolve infrastructure for benchmarking, evaluating, and deploying foundation and multimodal AI models. Partner with AI and data science teams to support rigorous, reproducible model development in a hybrid Ramat Gan role.
Responsabilidades
- Design, build, and maintain benchmarking suites for foundation models and multimodal AI systems
- Create extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows
- Implement metrics covering predictive performance, biological relevance, and multimodal alignment
- Collaborate with AI scientists and data scientists to integrate models, adjust architectures, and support iteration
- Add internal and external models and baselines to benchmarking workflows
- Analyze data and evaluation results to debug issues and understand model behavior
- Ensure evaluations are consistent, versioned, reproducible, and trustworthy
- Support model deployment and broader machine learning engineering infrastructure
Requisitos
- Bachelor’s, master’s, or doctoral degree in computer science, software engineering, or a related field
- At least 5 years of hands-on industry experience with machine learning models and evaluation pipelines
- Strong software engineering skills and experience designing maintainable, modular systems
- Proficiency in Python and modern machine learning ecosystems
- Ability to read, modify, and debug deep learning models
- Comfort working with complex datasets and conducting targeted exploratory analysis
Se valora
- Experience with benchmarks, metrics, or evaluation frameworks
- Familiarity with foundation models or multimodal learning
- Experience in biomedical or other data-intensive domains