Senior AI Research Engineer at Tenzai

Tel-Aviv, Tel-Aviv District, Israel -

Full Time

Start Date

Immediate

Expiry Date

08 Aug, 26

Salary

0.0

Posted On

10 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

LLM Training, Fine-tuning, Inference, Python, System Design, Data Modeling, Agentic Tooling, Context Engineering, Knowledge Management, Evaluation Pipelines, Backend Development, Debugging

Industry

Computer and Network Security

Description

About Tenzai Tenzai is building autonomous AI hackers that think like the world's best offensive security experts, act like them, and then help fix what they find before the real ones do. If this mission speaks to you, come defend the world's software: autonomously, continuously, and in-depth. About the role This is a rare opportunity to work at the intersection of offensive security and AI — and to have your expertise shape a platform used at enterprise scale. Your work will be deployed and used to hack into applications and systems in the real world. Responsibilites Build Tenzai’s Agent Harness - continuously deliver high impact features to improve the Tenzai Agent Harness - knowledge management. Context engineering and agentic tooling. Develop Data Systems for AI Memory - Build storage, retrieval and reasoning systems to enable agents to effectively use external knowledge and improve over time. Work on Experimentation and Evaluation Systems - Evals are key to everything we do, in this role you will build evaluation pipelines, design and build experiments and simulations. Working with Researchers and Scientists to deliver measurable improvements - repeatedly and consistently. Requirements Strong understanding of modern LLMs, including training, fine-tuning, and inference, with hands-on experience deploying LLM-based systems in production. Have built an agent, hands on. Experience with complex loops, context engineering, and long executions tooling. 5+ years of hands-on programming experience in Python or other backend languages. Strong fundamentals in system design, performance, and debugging. Experience in data systems, data modelling (databases, pipelines etc). Real world experience running experiments, measuring and iterating. Advanced Degree, research experience- An advantage Good understanding of modern, AI driven software development- An advantage Why Tenzai Working on problems that matter - Software is now written and deployed faster than it can be secured. Attackers are beginning to use AI to find vulnerabilities at scale. Tenzai exists to close that gap. It's a mission that requires elite hackers, and also sharp storytellers, wicked-smart operators, and experienced market builders. Building a category in the making - Tenzai is defining a new category in cybersecurity. That means every team — from research and engineering to product, brand, and go-to-market — has a chance to shape how the industry understands and adopts autonomous security. If you thrive building something from zero and helping a new idea take hold in the world, keep reading. Talent density - We believe small teams of exceptional people outperform large organizations. Tenzai was founded by veteran security experts and repeat entrepreneurs. We hire deliberately and value curiosity, initiative, and craftsmanship, whatever your discipline. Creating real impact, quickly - Whether you're building systems, shaping product experiences, telling the story, or helping customers succeed, your work will immediately influence how organizations secure their software. We move quickly, keep teams lean, and give humans ownership. Ambition with humility - We're tackling hard problems with a team that values deep expertise and thoughtful collaboration. Everyone here is learning, experimenting, and pushing the boundaries of what's possible. Plus, we love eating lunch together.

Responsibilities

Build and deliver the Tenzai Agent Harness, focusing on knowledge management, context engineering, and agentic tooling. Develop data systems for AI memory and create evaluation pipelines to measure and iterate on agent performance.