לוח משרות דרושים התמחות

We are a leading hybrid-cloud security company revolutionizing the way organizations manage cyber risk. We provide cutting-edge exposure management solutions by simulating real-world attack paths across cloud and on-prem environments. Our technology identifies misconfigurations, vulnerabilities, and identity exposures, allowing organizations to remediate risks with minimal effort. We are looking for a brilliant and motivated Product Management Intern to join our product team. This is a unique opportunity to sit at the intersection of Cybersecurity Research and Software Development, to add AI capabilities to the product. You wont just be watching. You'll be working with the Product group to brainstorm and TEST new AI-powered features for our platform.
Responsibilities:
* AI-driven research: use LLMs and AI tools to find better ways to explain and fix complex security issues. Use Python to analyze datasets and benchmark prompt performance to ensure AI accuracy in security contexts. Rapid Prototyping: Build functional Proof of Concepts (POCs) for AI Agents that automate complex security workflows. This includes enhancement to server-side attack simulation logic using Node.js, MongoDB.
* Work with Python and Node.js to develop innovative security solutions.
* Translate new cloud attack techniques into logic that our AI can simulate and explain.
* Collaborate with the development team to maintain and improve existing AI infrastructure.

Were currently seeking a Business Development Intern for a 3 months internship, available from July 1st to September 30th 2026, to contribute to our roadmap and make our collective strategic plans a reality! With this being our 4th year of running our Tricentis Internship program, we confidently commit to you that you will learn the foundations of SaaS / Tech marketing and sales, have a lot of fun along the way, and youll get access and partnership to industry leaders to set you up for success.
What youll contribute and learn as a Business Development Intern:
Learn prospecting skills by working directly with business development representatives, and sales and marketing leaders
Shadow various team members to including listening in on discovery calls, demos, and negotiations with prospects
Learn several different technologies as you do research to determine which companies to target and the appropriate roles within those companies
Work on data enrichment projects for both customers and prospects.
job requisition id JR105409.

Required PhD Machine Learning Research Intern
27984
Role Description
About the team: The internship is open for PhD students in a machine learning related field like Planning with LLMs, Multilingual Training Adaptation, Recommender System and Ranking, Bidding, Synthetic Data Generation, Entity Representation Learning, Reinforcement learning and Classification.
Applicants are asked to attach with their CV the email address of their supervisor or co-supervisor. This email will be used to ask for a recommendation letter at a later date.
Please complete your application before March 31st, 2026. Participation in the internship program requires that you are located in Israel for the duration of the internship program (3 months)
Role Description:
As a Machine Learning Research intern at Booking.com in Tel-Aviv, you will have the opportunity to tackle real world problems by pushing the boundaries of the state-of-the-art, with the goal of publishing your work.
You will join us for a 3-month internship (preferred start date: May) focused on Data-Centric AI. In this project, you will tackle the "glass ceiling" of NLU performance: inherent ambiguity in annotation guidelines and label taxonomies. You will move beyond traditional noise reduction to investigate why human annotators disagree and how these disagreements corrupt "ground truth," directly influencing how we evaluate and improve state-of-the-art models.
Key Job Responsibilities and Duties:
Survey the Landscape: Conduct a comprehensive literature review to synthesize current approaches to data ambiguity, identifying the strengths and limitations of existing methods in handling label noise and taxonomy gaps.
Innovate & Design: Devise novel techniques and metrics to detect, quantify, and resolve inherent ambiguities within NLP datasets and annotation guidelines.
Experiment & Validate: Design and execute rigorous experiments to evaluate your proposed methods, measuring their impact on model reliability and ground truth accuracy.
Publish Findings: Summarize your conclusions and contribute to writing a research paper with the aim of submitting to a top-tier NLP conference (e.g., ACL, EMNLP).