This role is designed for a researcher who is "hands-on." We value deep theoretical knowledge, but we require the ability to translate that theory into productive, working code within a limited timeframe.
What youll be doing
Long-Horizon Research: Lead specific, deep-dive research initiatives that require advanced methodology (e.g., novel anomaly detection architectures or LLM-based reasoning for security threats) without the pressure of daily sprint cycles.
LLM Innovation: Design and prototype advanced LLM workflows (RAG, Agents, Fine-tuning) to solve specific security challenges that standard APIs cannot handle.
Academic-to-Industry Bridge: Act as a knowledge hub for the team; bring SOTA academic concepts, recent paper findings, and novel techniques into the teams toolkit.
High-Impact Prototyping: Build functional Proofs of Concept (POCs) that the full-time engineering team can eventually operationalize.
Requirements: Current enrollment in a PhD program (Mathematics, Computer Science, Statistics, or related field) with a focus on Machine Learning, NLP, or AI.
Previous industry experience (internships or full-time) demonstrating the ability to work with noisy, real-world data.
Deep Practical Engineering: Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, Hugging Face).
LLM Expertise: Demonstrated experience working with Transformers and LLMs beyond simple prompting (e.g., experience with embeddings, vector databases, quantization, or fine-tuning).
Self-Starter: Proven ability to manage research projects independently with minimal supervision.
Communication: Excellent ability to explain complex mathematical concepts to engineers and stakeholders (English/Hebrew).
This position is open to all candidates.