We are looking for an ML Infra Engineer to play a central role in evaluating, and deploying our AI models. You will own and evolve our benchmarking and evaluation capabilities for foundation models and multimodal systems, while also working closely with modeling teams to support model development, iteration, and validation. This role sits at the intersection of software engineering, model understanding, and applied AI, with broad influence on how models are built, compared, and improved across the organization.
This is NOT a core algorithmic research role - but rather, a role focused on building out the ML engineering infrastructure for the evaluation and deployment of models.
Location: Ramat Gan, Israel (hybrid model)
What will you do?
Own & Evolve Benchmarking - Design, build, and maintain our benchmarking suite for foundation models and multimodal AI systems.
Define Core Abstractions - Create clean, extensible abstractions and APIs for datasets, tasks, models, metrics, and evaluation workflows.
Develop Metrics & Evaluations - Implement metrics that capture predictive performance, biological relevance, and multimodal alignment.
Support Model Development - Work closely with AI scientists and data scientists to integrate new models, tweak architectures, and enable rapid, fair iteration.
Bring in New Models & Baselines - Add external and internal models to benchmarks and ensure meaningful comparisons.
Explore Data When Needed - Dive into data and results to debug evaluations, understand model behavior, and unblock modeling work.
Enable Rigor & Reproducibility - Ensure evaluations are consistent, well-versioned, and trustworthy over time.
Requirements: Required qualifications:
BSc, MSc, or PhD in Computer Science, Software Engineering, or a related field.
Strong software engineering skills with experience designing maintainable, modular systems.
5+ years hands-on, industry experience working with ML models and evaluation pipelines - a must.
Proficiency in Python and modern ML ecosystems.
Ability to read, modify, and debug deep learning models.
Experience with benchmarks, metrics, or evaluation frameworks - preferred.
Familiarity with foundation models or multimodal learning - preferred.
Comfort navigating complex datasets and doing targeted exploratory analysis.
Experience in biomedical or other data-intensive domains - a plus.
Desired personal traits:
You want to make an impact on humankind.
You prioritize We over I.
You enjoy getting things done and striving for excellence.
You collaborate effectively with people of diverse backgrounds and cultures.
You have a growth mindset.
You are candid, authentic, and transparent.
This position is open to all candidates.