דרושים » תוכנה » IT Specialist (Part time )

Required Technical Support Specialist - Student Position
Description
Come Join us as a member of the Technical Support Team and part of our Operations, where you will take part in facilitating and managing technical support and monitoring.
Main Responsibilities:
Being part of our customer facing team responsible for technical support and troubleshooting
Working in a 24/7 support hours' shifts
Gathering detailed technical information as needed and investigating issues to solve problems, evaluate and escalate priority issues as needed.
Spotting common trends and underlying problems
Providing reliable resolution of critical and high impact problems for customers
Updating self-help documents so customers/employees can try to fix problems themselves
Working closely with our developers and technical teams to solve customers problems efficiently and improve our product
Creatively suggest and build software work-around solutions, including occasional scripting/ coding and customizations
Part and full-time/ weekends position available.

we are looking for a Customer Support Specialist.
As a Tier 1 Customer Support you will be responsible for:
Be the first line of defense and escalation for any technical issues
Elegantly handle customer requests and maintain SLA & customer satisfaction
Escalate and own issues internally and externally with service providers / partners
Extend our knowledge base
Work across departments (Engineering, DevOps, Prof Services, etc.) to resolve customers issues

As a Student Automation Developer, you will play a critical role in ensuring the quality, stability, and success of our core platform. This is not just a background role - you will take ownership of end-to-end test plans for complex, cutting-edge features (including AI sessions, data privacy tools, and query builders).

You will work closely with experienced developers, Product Managers, and Data Scientists, gaining deep technical exposure that serves as an excellent stepping stone toward a future career in FS or BE software engineering.

Key Responsibilities
Automation Development: Design, build, and maintain robust automated testing frameworks and scripts, primarily using Python.
Feature Ownership: Take the lead on creating and executing comprehensive test plans for newly released features, ensuring product stability from day one.
CI/CD Pipeline Management: Monitor daily CI/CD pipelines to catch regressions early, triage failures, and maintain a highly reliable release cycle.
API & Backend Testing: Conduct deep backend validation by interacting with REST APIs, utilizing tools like Postman and internal automation clients.
Cross-Functional Collaboration: Partner directly with the Platform, Product, and Data Science teams to clarify requirements, investigate bugs, and define expected system behaviors.
Continuous Improvement: Actively participate in framework refactoring, workflow improvements, and writing clean, maintainable code.

we are looking for a PhD Machine Learning Research Intern.
As a Machine Learning Research intern 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).
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