לוח משרות דרושים הייטק AI  בירושלים

we are looking for a AI Security Engineer.
As AI capabilities accelerate across the bank, we need an engineer to design and enforce safe AI usage-protecting customer data, preserving model integrity, and meeting our regulatory obligations. You'll be the architect of guardrails, tooling, and policies that make AI both secure and useful for product and internal teams. This isn't about slowing things down; it's about building the trust layer that lets innovation move fast without breaking things.
What Youll Actually Be Doing:
Design enterprise AI guardrails across Azure and AWS (e.g., Azure AI Studio/Azure OpenAI, Amazon Bedrock/SageMaker): content filtering, PII redaction, prompt/response validation, and policy enforcement services.
Implement data minimization controls for GenAI/RAG workloads: context filtering, least‐privileged retrieval, document-level ACL enforcement, vector store hardening, and secure token/secret handling.
Threat model AI systems (apps, agents, RAG, fine-tuning pipelines) using frameworks like STRIDE and the OWASP Top 10 for LLM Apps; define misuse scenarios (prompt injection/jailbreaks/data exfiltration) and build mitigations.
Build monitoring and telemetry: privacy-preserving prompt/response logging, sensitive-data detection, safety/eval dashboards, drift/abuse signals, and incident hooks into our SIEM.
Integrate AI security into the SDLC: reusable libraries, pre-commit checks, CI/CD gates, policy-as-code, and secure-by-default reference architectures for product teams.
Evaluate third‑party AI vendors and internal apps: security reviews, data residency and retention requirements, SSO/SCIM integrations, DPA/TPRM inputs, and continuous control testing.
Partner across Security, Data, Privacy, and Engineering to map AI controls to FFIEC, SOC 2, and PCI DSS; document control evidence for audits.
Lead/participate in AI red‑teaming: automated jailbreak/prompt‑injection tests, safety benchmarks, purple‑team exercises, and response playbooks for AI incidents.
Enable the org with concise guidelines, examples, and training on safe AI development and usage.

the corporate genai lead drives accelerates and scales genai adoption across our company to deliver measurable business impact.
this role translates emerging ai capabilities into structured, secure and business driven, working cross-functionally with r&d, it, Cyber security, and corporate teams, leading vendor relations and evaluating new ai solutions.
the genai director will operate at the intersection of technology strategy and execution - aligning priorities, enabling use cases, and shaping the genai roadmap to support our sustainable growth. what will your job look like: identify and prioritize high-impact genai use cases across engineering and corporate functions, aligning initiatives with measurable business value
lead cross-functional initiatives from concept through pilot and scalable deployment, ensuring clear ownership and delivery milestones
define and evolve the genai platform and infrastructure roadmap to support secure, scalable, and cost-efficient adoption
partner with it, Cyber security, and engineering teams to ensure secure and compliant implementation.
evaluate external tools, lead the adoption lifecycle from poc to enterprise rollout, and drive data -driven build-or-buy decisions
track adoption, business impact, kpis, and roi across the genai portfolio, adjusting priorities as needed
lead the genai internal community, strengthen internal knowledge sharing, and ai standards responsibilities.

we're building the financial infrastructure that powers global innovation. With our cutting-edge suite of Embedded payments, cards, and lending solutions, we enable millions of businesses and consumers to transact seamlessly and securely. With 900+ employees worldwide and an R&D center of over 160 employees in Jerusalem - were reshaping how financial technology is developed and delivered..
The Role:
As AI capabilities accelerate across the bank, we need an engineer to design and enforce safe AI usage-protecting customer data, preserving model integrity, and meeting our regulatory obligations. You'll be the architect of guardrails, tooling, and policies that make AI both secure and useful for product and internal teams. This isn't about slowing things down; it's about building the trust layer that lets innovation move fast without breaking things.
Who You Are:
You're a security engineer who's excited about the AI wave-someone who sees GenAI and LLMs as fascinating puzzles to secure, not just threats to mitigate. You've spent 5+ years in Security Engineering, AppSec, or Cloud Security, and at least 1-2 of those years have been spent getting your hands dirty with AI/ML or data -intensive systems. You're equally comfortable dissecting a prompt injection attack as you are writing a Terraform module or shipping a Python library. You know your way around AWS and/or Azure, modern app stacks ( Python /TypeScript, REST/gRPC, containers/Kubernetes), and can translate security requirements into Developer -friendly tooling-not just PDF policies that gather dust. You communicate clearly in English and Hebrew, thrive in regulated environments, and understand that security in financial services means mapping controls to frameworks like FFIEC, SOC 2, and PCI DSS-and actually having the evidence to prove it.
What Youll Actually Be Doing:

* Design enterprise AI guardrails across Azure and AWS (e.g., Azure AI Studio/Azure OpenAI, Amazon Bedrock/SageMaker): content filtering, PII redaction, prompt/response validation, and policy enforcement services.
* Implement data minimization controls for GenAI/RAG workloads: context filtering, leastprivileged retrieval, document-level ACL enforcement, vector store hardening, and secure token/secret handling.
* Threat model AI systems (apps, agents, RAG, fine-tuning pipelines) using frameworks like STRIDE and the OWASP Top 10 for LLM Apps; define misuse scenarios (prompt injection/jailbreaks/ data exfiltration) and build mitigations.
* Build monitoring and telemetry: privacy-preserving prompt/response logging, sensitive- data detection, safety/eval dashboards, drift/abuse signals, and incident hooks into our SIEM.
* Integrate AI security into the SDLC: reusable libraries, pre-commit checks, CI/CD gates, policy-as-code, and secure-by-default reference architectures for product teams.
* Evaluate thirdparty AI vendors and internal apps: security reviews, data residency and retention requirements, SSO/SCIM integrations, DPA/TPRM inputs, and continuous control testing.
* Partner across Security, data, Privacy, and Engineering to map AI controls to FFIEC, SOC 2, and PCI DSS; document control evidence for audits.
* Lead/participate in AI redteaming: automated jailbreak/promptinjection tests, safety benchmarks, purpleteam exercises, and response playbooks for AI incidents.
* Enable the org with concise guidelines, examples, and training on safe AI development and usage.

Why Youll Love Working Here:

* Flexible hybrid work model: three days a week at our Jerusalem office
* Monthly wellness reimbursement - from therapy to gel manicure, it's up to you
* Full Keren Hishtalmut, private health and dental insurance
* Volunteer days, donation matching, Yoga and Pilates
* A supportive, collaborative culture that puts our people first
Next Step:
Hit Apply!

Were looking for a Creative AI Solutions Specialist to join our AI Lab - a cross-functional group driving innovation, experimentation, and advanced AI capabilities across .

This role sits at the intersection of creative technology, product, and applied AI. Youll work on cutting-edge LTX-2 workflows, prototype new capabilities, help bring AI solutions to market, and act as a domain expert supporting teams across the company - from Product and Design to Sales and Business Development.

Success in this role requires strong ownership, curiosity, technical depth, and a passion for pushing creative AI forward.

What you will be doing
Build AI-driven creative workflows, PoCs, and production-ready pipelines (with a strong focus on LTX-2)
Experiment with generative AI to unlock new creative capabilities and solve high-end production challenges
Create workflows and custom LoRAs to showcase capabilities to clients and design partners
Partner with Business Development and major studios, providing technical demos and pipeline integration guidance
Act as a trusted technical advisor for clients and internal teams
Collaborate with Product, Research, Design, and Creative teams to turn innovation into impactful features
Own the ideation and exploration of new creative and AI-driven capabilities, driving initiatives from concept and research to tangible prototypes and production impact

The team focuses on art direction, prompt architecture, and technical experimentation, bridging the gap between creative design and AI tool development inside LTX Studio platform.

Its responsibilities include feature development, testing generative models, building LLM-driven systems for visual generation, and defining visual presets, using the team creative background to bring out-of-the-box thinking and artistic perspective to technical features and innovation.

What you will be doing
Own visual quality and creative evaluation across image or video domains within LTX Studio
Lead AI Evaluation & AI Creation efforts - testing new generative models, benchmarking outputs, identifying strengths and limitations
Build POCs using AI coding tools and vibe coding methodologies
Translate innovation ideas into demos, workflows, and product features (0→1 ownership)
Develop and refine prompt engineering systems, including prompt enhancement logic and structured generation rules
Improve and iterate on existing product features related to visual generation
Collaborate closely with PMs, developers, researchers, and cross-functional teams
Participate in feature ideation and bring creative-first thinking into technical product discussions
Help define visual presets, output standards, and quality benchmarks
Continuously explore emerging tools, models, and techniques in the AI creator ecosystem

We are seeking a researcher to join the LTX Model Applications team, focusing on advancing and extending the capabilities of our LTX-Video models. You will design and explore new use cases, train novel ways to control the generation process, and push the boundaries of generative video technology. Your work will directly influence millions of users through our apps and contribute to the broader research and open-source communities, shaping the next generation of creative tools.

What you will be doing
Advance and extend the capabilities of our LTX-Video models through new controls, features, and use cases.
Design and implement machine learning models for video, image, and audio generation, as well as multimodal applications.
Conduct experiments, prototype concepts, and validate ideas to expand generative video technology.
Collaborate with engineers, designers, and product managers to translate research into impactful product features.
Regularly open-source code, models, and research insights, contributing to the academic and open-source communities.
Stay at the forefront of generative AI research in video, image, and audio, and bring relevant advances into our products.
Write high-quality, efficient code and maintain best practices in research workflows.
Mentor other researchers and lead collaborative projects.

The Model Evaluation team is the central nervous system of the LTX Foundation Model group. We don't just measure performance; we define what "good" looks like across a vast array of use cases. While we power the next generation of creative tools, LTX is also a foundational engine for simulation pipelines, game engines, synthetic data generation, architectural rendering, and digital avatars. We act as the critical bridge between raw research and industrial-grade reliability, building the benchmarks that ensure our models are world-class for both artists and engineers.

The Role
As a Research Scientist in Model Evaluation, you are the ultimate authority on model quality and utility. You will design the automated judges, reward models, evaluation datasets, and benchmarking ecosystems that determine the future of LTX. Your mission is to provide the "ground truth" for our pre-training and post-training teams. You will blend the rigor of a researcher with the intuition of a product-thinker, developing metrics that capture both the aesthetic soul of a video and the functional precision required for high-stakes professional use.

Key Responsibilities
Steer Training & Research: Systematically evaluate model checkpoints to provide actionable insights that guide training experiments and architectural decisions.
Design Benchmark Ecosystems: Develop and run rigorous benchmarks for release candidates against competitive models, ensuring LTX-2 remains world-class.
Build Next-Gen Metrics: Develop robust automatic metrics and Reward Models (e.g., for RL, ITS, auto-research agents) that quantify complex attributes like temporal coherence, physical correctness, spatial accuracy, and foley synchronization.
Diagnose & Analyze: Perform deep root-cause analysis on model failures, providing the diagnostic clarity needed for researchers to implement targeted fixes.
Scale Evaluation: Collaborate with platform engineers to deploy evaluation frameworks across large-scale GPU clusters.

This role is designed for individuals who are not only technically proficient but also deeply passionate about pushing the boundaries of AI and machine learning through innovative engineering and collaborative research.

Key Responsibilities
Profile and optimize the training process to ensure efficiency and effectiveness, including optimizing multimodal data pipelines and data storage methods.
Develop high-performance TPU/GPU/CPU kernels and integrate advanced techniques into our training framework to maximize hardware efficiency.
Utilize knowledge of hardware features to make aggressive optimizations and advise on hardware/software co-designs.
Collaboratively develop model architectures with researchers that facilitate efficient training and inference.
Design, maintain, and evolve a high-quality, shared codebase that emphasizes correctness, readability, extensibility, testing, and long-term maintainability, while balancing performance requirements.

This role focuses on pioneering model architecture and pre-training algorithms, shaping the next generation of our foundational generative AI models.

What you will be doing
Pre-train and fine-tune video, audio, and image generative models to pursue state-of-the-art results.
Publish papers and open source models to benefit the research community and advance the field.
Design and implement machine learning models for text-to-audio and text-to-video generation.
Collaborate with data engineers to curate and preprocess text and video data.
Optimize models for high performance, ensuring efficient training and inference.
Build new controls and capabilities into generative text-to-audio and text-to-video models.
Stay updated with the latest developments in Generative AI, particularly in the fields of image, video, and audio.
Work closely with product teams to integrate AI models into applications and services.
Conduct experiments and prototype new concepts to advance the capabilities of our AI tools.