דרושים » הנדסה » Technical Policy Researcher

We are hiring an AI Researcher to join the research team building the next generation of AI-native security systems. You will work alongside security and threat researchers to build large-scale AI agents that reason over software, code, endpoint activity, and security signals to detect malicious behavior, uncover vulnerabilities, assess risk, and make autonomous security decisions in real-world production environments. We are entering the Mythos era - where attackers operate at machine speed using autonomous systems and AI-generated software, and defenders must evolve the same way. We use state-of-the-art frontier models, including access to Mythos, to build reliable AI-native security systems at global scale. You will help design the evaluations, harnesses, and reliability infrastructure that make autonomous agents dependable under real customer load, while collaborating with leading AI organizations including Anthropic on initiatives such as Glasswing. This is an opportunity to work at the frontier of AI, autonomous systems, and cybersecurity while helping define how the next generation of security systems will operate.
Key Responsibilities
Build AI agents and autonomous security systems that reason over software, code, endpoint activity, MCPs, and security signals to detect malicious behavior, uncover vulnerabilities, and assess risk at production scale.
Develop systems, tooling, and infrastructure that enable agents to autonomously investigate threats, hunt for malware in massive datasets, and operate reliably in complex security environments.
Design and run experiments to evaluate frontier-model and agent capabilities in realistic adversarial scenarios, including benchmark creation, large-scale datasets, automated evaluations, and human-in-the-loop review systems.
Build the evaluation harnesses, observability systems, and reliability infrastructure required to make autonomous agents accurate, scalable, and dependable under real customer load.
Engineer for scale and performance across large distributed AI systems, including inference optimization, orchestration, batching, caching, cost controls, and graceful degradation under high demand.
Continuously evaluate emerging models, agent architectures, prompting techniques, and research directions to ensure our systems remain at the frontier of AI-native cybersecurity.
Rapidly prototype and test new approaches across reasoning, autonomy, evaluations, and security workflows as the AI landscape evolves.
Partner closely with threat and security researchers to extract domain expertise, translate analyst reasoning into AI workflows, and enable new forms of automation and autonomous investigation.
Collaborate with leading AI and security researchers to shape the future of AI-native cybersecurity as the industry transitions into the Mythos era.
Senior candidates will help define research direction, shape technical strategy, identify high-leverage problems, and influence how autonomous AI systems are deployed across the organization.

As an AI Engineer at our company, you will own the intelligent decision-making pipelines that turn complex workspace telemetry into autonomous security actions. You will design, build, and deploy the autonomous reasoning workflows and advanced data classification systems that drive our company's preventive operating model. Your focus will be on creating resilient, production-grade AI systems capable of deep policy comprehension, real-time prevention at the point of adoption, and autonomous remediation of existing risks.
This is a ground-floor opportunity to shape the AI strategy of a fast-growing cybersecurity company alongside a lean, elite team of builders.
WHAT YOULL DO
End-to-End Ownership: Own our AI capabilities entirely from initial research, architectural design, and prototyping, through to production deployment, optimization, and continuous monitoring.
Design & Build Agentic Workflows: Architect multi-step AI agents capable of autonomously investigating workspace risks, interpreting complex enterprise policies, and taking precise remediation actions.
Integrate Multi-Faceted ML: Bring innovation and creative thinking to our core engine. Implement diverse ML models across our entire research and product pipeline-utilizing clustering, text extraction, document analysis, and tabular data classification.
Ship Production-Grade AI: Build high-throughput, resilient, and fault-tolerant production code. You will ensure our AI pipelines and agentic workflows are highly predictable, deeply observable, and built to scale under enterprise-grade loads.
Implement Guardrails & Evaluation: Build continuous evaluation frameworks to benchmark agent accuracy, mitigate hallucinations, and enforce strict data security/privacy guardrails.

Are you an analytical person who is deeply interested in AI and LLM technology?
Is your superpower accurately categorizing information and identifying patterns?
Do you love to solve interesting challenges and make a direct impact through your work?
Join our Tel Aviv team as an AI Data Analyst and help shape the future of AI. We're looking for analytical minds to contribute to our cutting-edge Large Language Model (LLM) project. In this unique role, you'll be performing LLM data quality tasks, ensuring consistency and quality in our conversations and helping design and support our flagship products.
This is more than a traditional data-labelling role. You will combine hands-on evaluation work with analytical thinking to help shape how our AI experiences evolve.
Sound like your perfect fit? We want to hear from you!
As an AI Data Quality Analyst, you will evaluate LLM-generated conversations and other text-based data, identify trends and regressions, and partner with AI Engineers and Product Managers to improve model performance. In this role, you will:
Review and evaluate LLM-generated conversations and other text-based outputs against task-specific quality criteria and success metrics.
Perform high-quality annotation and data labeling to create reliable datasets for model training, benchmarking, and evaluation.
Monitor real production data to identify quality trends, regressions, and areas for improvement.
Identify qualitative patterns, recurring failure modes, regressions, and opportunities to improve model behavior and user experience.
Analyze evaluation results and clearly communicate insights, risks, and recommendations to cross-functional stakeholders.
Work closely with our AI Engineers and Product Managers to understand the data needs and ensure data quality processes align with goals, ensuring Paradox and our company products remain top in class.

we are looking for a AI Algorithm Tech Lead.
This is a role in core AI group, which specializes in developing cutting-edge AI solutions in healthcare, enhancing medical imaging and diagnostics. As a Tech Leader in the AI Algorithm Engineering group, you will play a pivotal role in both managing end-to-end AI research and development efforts and hands-on involvement in key-initiatives. Your responsibility will include design, implementation, guidance and mentoring for team members, and representing the AI considerations in cross-team discussions. You will leverage your extensive experience to lead multiple AI projects, and will be a key figure in leading successful execution and in finding creative solutions to difficult bottlenecks, across data, AI, engineering and product considerations.
Responsibilities:
Lead the design, development, and implementation of accurate and robust AI algorithms for medical data with a hands-on approach.
Collaborate with cross-functional teams, including data scientists, software engineers, product managers and clinical data experts, to ensure AI considerations have a profound impact on R&D and Product decisions across the projects.
Design and conduct research and experimentation to improve algorithm performance and accuracy.
Stay at the forefront of the latest advancements in AI and machine learning, particularly in the medical domain, and drive their adoption within the team.
Oversee multiple projects, ensuring successful execution, timely delivery and alignment with product and business objectives.
Provide strategic direction and technical mentoring to elevate the capabilities of the AI team.

As an AI Researcher on Research group, youll research, develop and productionize LLMs powered applications and AI-agents. Youll partner closely with Product and Engineering to turn open-ended data-security challenges into measurable experiments and shipped features. Youll own the end-to-end lifecycle - from problem framing and data strategy to evaluation, deployment, and ongoing monitoring - helping customers discover, protect, and govern their data at scale.



What Youll Do

Responsible for the end-to-end research process. This includes identifying problems, preparing data, tuning and developing models, deploying to production, and analyzing outcomes.
You will be a hands-on domain leader, laying the foundations of our data science workflows and algorithms. This is an excellent opportunity to work with endless amounts of data and creatively generate insights that will increase the ability to classify tons of data.
Develop, evaluate, and maintain deep learning and NLP solutions to enhance core capabilities in sensitive data classification.
Design and architect production-grade agentic workflows. Establish rigorous evaluation pipelines to benchmark agent accuracy, latency, and cost, ensuring reliable, scalable solutions for real-world customer problems
Innovation and creative thinking are the keys! Implementing ML models to the entire research process - clustering, text extraction, document analysis, and tabular data classification.
Join a full stack AI group, including research engineering, MLEs, data operations, and security researchers. You will accelerate the path from research to production, ensuring results are both quick and precise.

Are you a master of the AI lifecycle who can thrive at the intersection of deep learning research, cybersecurity and large-scale production systems? Do you possess the rare ability to jump into a complex technical crisis, diagnose a bottleneck in a Small Language Model (SLM), and lead a team to a production-ready solution?
As a Distinguished AI/ML Architect, you will be the premier technical authority across the Cortex AI/ML organization. You will serve as the "force multiplier" for multiple AI & ML teams, ensuring that our AI strategy - from endpoint-deployed DL/ML models to cloud-scale agentic products - is executed with world-class precision. You will operate as the primary technical architect and hands-on leader for our most ambitious and difficult initiatives.
Key Responsibilities
Operate at the forefront of AI and cybersecurity, leveraging vast datasets to design and deploy innovative defense mechanisms.
Provide horizontal technical leadership across endpoint, cloud, and agentic AI teams to ensure architectural excellence.
Oversee the design and deployment of different model architectures to guarantee scalability and high-performance standards.
Spearhead high-difficulty initiatives and new research frontiers, moving them from ambiguity to production.
Resolve complex technical bottlenecks across the stack, optimizing model efficiency and runtime performance.
Align research and engineering efforts to transform advanced models into robust, production-grade products.

Own the intelligence layer - the AI research pipeline that classifies and risk-scores every tool we find. Discovery tells us what software an organization runs; the AI does the hard part: figuring out what each tool actually is, what it can do, how it handles data, and how risky it is. You'll own that research and enrichment system end to end - the LLM-backed agents, the prompts and context that drive them, the evals that keep them honest, and the cost and latency of running them at scale.

This is an engineering role, not a research one. You'll ship production TypeScript, and you'll be measured on the accuracy, cost, and reliability of the intelligence the product depends on.

What you'll work on

The multi-agent researcher system: LLM-backed agents that research each tool across topics like platform, data policy, AI models, and agentic capabilities, and return structured, evidence-backed classifications.

Evals and quality: design eval sets, measure classification accuracy and hallucination, and turn prompt changes into regression-tested, reviewable diffs instead of guesswork.

Grounding and trust: cite evidence, resolve contradictions between AI output and validated data, and drive down hallucination on the fields that matter.

Model routing and cost/latency: choose and route across providers, tune concurrency and caching, and keep the pipeline fast and affordable as volume grows.

Structured outputs, tool/function calling, and the schemas and validation that make model output safe to persist.

Deep observability into the pipeline - spans, traces, and metrics for every model call.

1. AI Architecture & Technical Leadership

Guide AI architectural direction across platform, focusing on system design, model lifecycle, and integration of AI components into product workflows.
Act as a senior technical reviewer and thought partner for complex or cross-team AI design decisions.
Provide technical oversight on areas such as exploring new models/technologies/opportunities for the company
Surface architectural risks, tradeoffs, and long-term implications, including cost, security and compliance aspects and clearly advise the VP of AI when certain technical directions should not be pursued.
This role influences judgment, clarity, and experience, not through blocking authority.

2. Hands-on Applied Innovation (Core Pillar | ~50%)

Spend at least 50% of time hands-on, building:
End-to-end AI prototypes
Technical demos and proofs of concept
Exploratory implementations of new AI capabilities
Drive applied innovation that:

De-risks new technologies
Demonstrates feasibility and impact
Informs product direction and business opportunities
Build fast, concrete examples that teams can learn from and extend.
Transition successful prototypes to team ownership for further development and scaling.
This role is expected to lead AI innovation by doing, while working closely with product, medical and engineering

3. Best Practices & Technical Enablement

Define and promote best practices for applied AI development, including:
Rapid prototyping and vibe coding.
Agent design, orchestration, and evaluation patterns
Experimentation, benchmarking, and validation workflows
Help teams align on shared technical patterns, tools, and standards.
Identify opportunities to consolidate duplicated efforts and improve cross-team coherence.
Lead technical deep dives, architecture discussions, and design reviews.
4. AI Compliance & Regulatory Enablement (Technical Scope)

Ensure Navinas AI development practices align with applicable AI regulations for a software product handling sensitive medical data.
Define and guide AI-specific compliance practices, including data usage, transparency, evaluation, and documentation expectations.
Support and contribute to AI-related compliance and regulatory documentation, in close collaboration with Legal, Security, and Medical Research teams.
Serve as a technical point of reference for AI compliance questions.

We are looking for an AI Research Engineer to build the backbone of our autonomous security platform.
our company builds AI Digital Employees who help cyber teams close the execution gap. Our first AI Digital Employee, Alex, learns, understands and takes away the burden of Identity and Access Management (IAM) tasks - proactively completing the organization's cyber objectives.
About the Role:
You'll own technically difficult problems before the solution is known. Starting with a cybersecurity need, you'll study the domain, frame the technical challenge, investigate possible approaches, and build the solution through production. You'll have access to our company's shared agent infrastructure, but the role goes far beyond configuring existing components. Many use cases will require new reasoning methods, agent architectures, evaluation techniques, data strategies, or model behavior. This is a role for someone who enjoys applied research but is motivated by outcomes. The goal is not simply to prove that an idea can work, it is to make it useful and reliable for customers.
What You'll Do:
Design and implement AI solutions for complex cybersecurity workflows.
Develop new approaches for agent reasoning, planning, tool use, context management, decision-making, and recovery from failures.
Determine the right technical approach for each problem, combining models, algorithms, data, and conventional software where appropriate.
Define quality for each use case and create evaluations that measure correctness, task completion, consistency, safety, latency, and cost.
Analyze agent behavior deeply, identify the underlying causes of failures, and improve the relevant model, data, tool, or architecture.
Collaborate with cybersecurity experts, product teams, software engineers, and AI infrastructure engineers throughout development.

we are looking for an experienced, independent team player who has great data & computer science skills, a passion for data, and excellent analytical and algorithmic skills.
This role is diverse, encompassing AI/ML, data analysis, and backend engineering.
You will play a crucial role within a mission-critical team responsible for managing the heart and brain of primary products. This involves working on core systems, such as POS underwriting models, models for merchant operations, Fraud investigator AI agents and more.
You will participate in cutting-edge risk management systems that safeguard the loan product's financial operation, as well as operational systems, with a direct impact on its financial success.
Our AI/ML team is part of the R&D group, so you will work closely with engineers and product managers as well.
Key Responsibilities
Research and develop statistical behaviors, study domain-specific data.
Develop state-of-the-art machine learning models end to end, including development, deployment, and continuous improvement. Both in-weight learning and in-context learning, for risk / fraud / operations related projects. This includes integrating models into production services and ensuring compliance with regulatory processes (e.g. providing evidence for production model audits).
Operate backend infrastructure for training and deploying ML models, ensuring optimal performance and reliability.
Develop and maintain Python code for translating ML model outputs into financial decisions.
Analyze 15+ different data sources in order to train models and agents to catch fraudulent patterns.
Conduct analytical research on our models impact on the portfolio.
Strategize and implement changes to enhance portfolio performance.