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Senior ML Infrastructure Engineer
HybridFull-timeAI 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.
Responsibilities
- 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
Requirements
- 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
Nice to have
- Experience with benchmarks, metrics, or evaluation frameworks
- Familiarity with foundation models or multimodal learning
- Experience in biomedical or other data-intensive domains