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A fast-growing global medical device company, developing and manufacturing innovative drug delivery and infusion solutions across the continuum of care- from the hospital to the home. We are looking for an excellent Production Quality Engineer to join the QA product team.
Job Description:
Serve as owner of the NCMR process: manage the end-to-end handling of non-conforming materials, products, and processes, including initiation, investigation and corrective/preventive actions in accordance with company procedures and regulatory requirements.
Monitor NCMR process including quality KPIs
Support for non-conformity investigations and assign effective corrective actions for NCMRs and ensure timely closure and documentation.
Perform / review Root Cause Analysis as part of NCMR process and assign relevant corrective actions.
Act as QA representative for Manufacturing Quality process
Conduct quality assurance activities such as data analysis and statistical evaluations, SOP review and approvals, audit preparation
Direct Manager: Manufacturing Quality Manager

We are looking for a Senior AI Researcher to work closely with our delivery team, translating real-world clinical and scientific questions into rigorous AI and machine learning solutions. In this role, you will lead the development of methodologies, models, and benchmarking frameworks that address complex clinical problems using our unique immune-cell data. Your work will directly impact how our partners understand biology, evaluate hypotheses, and make decisions.

Location: Ramat Gan (hybrid model).

What will you do?
Partner with the collaborations team and scientists to frame complex clinical and biological questions as well-defined data science and modeling problems.
Design, implement, and evaluate the right analytical and modeling approaches for each problem, including robust benchmarking and validation strategies.
Clearly communicate results, limitations, and tradeoffs to internal teams and external partners, enabling confident, data-driven decisions.
Establish best practices for rigor, reproducibility, evaluation, and documentation across delivery-focused data science work.
Work hand-in-hand with immunologists, computational biologists, AI scientists, and engineers to ensure solutions are scientifically sound and production-aware.
Translate modeling solutions into engineered and iterable products used by the internal collaborations team.