לוח משרות דרושים הייטק AI , לחיפוש זה נמצאו 302 משרות

We're hiring a part-time assistant to support day-to-day operations on our AI voice-agent training platform. This is a hands-on, junior-level position with training provided, a strong fit for a student looking for practical experience with AI systems.
- Draft and refine the instructions that shape how our agents behave
- Support the creation of new agent demos
- Review agent interactions for quality and flag issues
- Help maintain and update content used by our AI agents
- Work closely with the technical team to learn and apply our processes

We are looking for a Senior Data & AI Engineer to join a new and growing Data & AI function. You'll work hands-on with models and data architecture, with direct impact on both product-facing AI capabilities and internal data infrastructure. This is a deeply technical role: you'll take ML models from research through production, design scalable pipelines, and collaborate closely with R&D and Product to solve real security problems with AI.
Responsibilities:
Design and implement data architecture, pipelines, and governance foundations
Develop and deploy ML models into production (not just POCs), focusing on areas like vulnerability detection, attack emulation, and security insights
Own model lifecycle end to end: research and experimentation, production deployment, monitoring, and iteration
Partner with Product and R&D to identify and execute high-impact AI use cases within our platform
Evaluate build vs. buy vs. fine-tune decisions for AI capabilities
Define and maintain data quality standards and access policies.

We are looking for a highly technical Senior Product Manager with deep expertise in data platforms and AI/ML to join our Product team. This is a unique opportunity for a data and AI expert to take full ownership of a strategic product domain, shaping how we leverage data intelligence and AI-driven capabilities to deliver smarter, faster security validation.
Roles & Responsibilities:
Lead product management activities throughout the entire product lifecycle in a fast-paced startup environment.
Drive the Data & AI product strategy, including defining AI model behavior/integration within cybersecurity, and leading data analytics to generate actionable product insights.
Collaborate closely with R&D, Sales, and customers to execute the roadmap, gain market insights, and drive revenue/ongoing product success.

A security company, is building an internal AI capability across the organization: agents that perform real tasks as the organization, with ROI and security assessed at every step. We are looking for a senior engineer who owns this end to end - a hands-on builder who also carries the senior function: setting the technical strategy, making the architecture calls, and being the person leadership trusts on internal AI.
This is not a pure IC build role and not a pure strategy role. You will personally build the flagship systems - production agents, the context layer, the evaluation and safety stack - while also deciding what gets built, in what order, and to what standard. You will work across R&D, IT, Security, BizOps, Customer, Sales, Marketing, People, and Finance, with the AI Transformation Officer owning the business portfolio.
What you'll do - strategy and senior function:
Own the technical strategy for our internal AI transformation: target architecture, model and vendor choices, build-vs-buy calls.
Shape the AI portfolio: which workflows to automate, in what sequence, with what success metrics - and push back when the wrong thing is being built.
Set and enforce the bar on ROI and security for every AI system: cost, adoption, cycle-time impact, access control, and auditability are reviewed per project, not after launch.
Act as the senior technical voice for internal AI: advise leadership, review AI initiatives across the org.
Turn one-off builds into a real internal platform: reusable patterns, shared services, and standards that AI Champions and teams adopt.
Mentor AI Champions; raise the org's practical AI level.
Manage our AI- Champions Community.
Continuously evaluate models, vendors, and frameworks, and keep us agile.
What you'll do - hands-on build:
Personally design, build, deploy, and own the flagship internal agents and AI workflows, from prototype through production.
Build and evolve our AI context layer: connectors, APIs, retrieval services, metadata, identity and authorization, controls, and tool interfaces.
Create safe action tools for agents and infrastructure for agents to work.
Build RAG, tool-calling, structured-output, and multi-step agentic workflows using the right model and architecture for each problem.
Own the production quality bar for what you ship: evaluation datasets and automated checks (accuracy, groundedness, task completion, safety, cost etc.)
Work directly with business owners to map processes, test with users, and drive each pilot to a clear scale-or-stop decision.

We're looking for an AI Security Researcher with a strong AppSec foundation. At the core, this is a deeply technical application security role at an engineering level. You'll sit with development teams, get involved in their processes, understand how software is actually built, and bring a security perspective from the inside. You'll enhance traditional AppSec with agentic tools and security layers, working directly with enterprise customers on hands-on engagements. On top of that defensive foundation, you'll layer AI-specific offensive capabilities: researching and executing attacks against AI/ML systems.
This is a unique opportunity to combine strong AppSec fundamentals with cutting-edge AI red teaming and agentic consulting, with a clear growth path as a researcher.
Roles and Responsibilities:
Conduct deep, engineering-level application security work: review architectures, assess code, and embed with development teams as a technical peer throughout the development lifecycle
Lead AI-focused security assessments and red team engagements for enterprise customers, identifying vulnerabilities in AI/ML systems, agentic workflows, and LLM-based applications
Enhance traditional AppSec processes with agentic tools and AI security layers
Research and develop novel attack techniques targeting AI/ML systems, including prompt injection, model manipulation, and agentic framework exploits
Translate offensive findings into actionable security recommendations for customers and internal teams
Stay at the forefront of AI security: continuously research emerging attack surfaces and contribute to the growing body of AI threat knowledge.

The Director of AI Enablement is both a builder and a transformation leader, someone who can identify opportunities, design and implement AI solutions, and drive adoption across the organization to create measurable business value.

Reporting directly to the CEO, this is a highly visible, hands-on leadership role at the intersection of business transformation, technology, and organizational change.

The successful candidate will lead AI roadmap, build practical AI solutions, drive adoption across functions, and help transform how work is performed throughout the organization.

What Will You Do?

Define and execute AI roadmap.

Identify, prioritize, and implement high-impact AI opportunities across the business.

Partner with business, operational, engineering, and corporate functions to understand workflows, pain -points and improvements opportunities.

Translate business challenges into practical AI use cases and measurable outcomes.

Design, build, pilot, and scale AI-powered solutions, including AI agents, copilots, automations, knowledge assistants, and business applications.

Partner with IT, Engineering, Infrastructure, and Security teams to ensure scalable, secure, and sustainable solutions.

Lead the transformation, not just the technology; drive AI adoption across departments and help leaders redesign processes and ways of working to maximize AI value.

Work hand-in-hand with HR and business leaders to lead the organizational transformation required to successfully adopt AI, build internal capabilities, and embed new ways of working across the company.

Establish and lead the core pillars of the AI Enablement program: Adoption, Infrastructure, and Solution Development.

Build and lead a community of AI Builders and Leaders across the organization.

Establish AI governance, Responsible AI practices, and security standards.

Measure and communicate business impact through adoption, productivity gains, efficiency improvements, time savings, and ROI.

Success is measured not only by building AI solutions, but by driving meaningful improvements in productivity, efficiency, quality, decision-making, and business performance across the organization.

Required Principal AI Security Engineer
Job Description
Role
This is a hands-on architecture role: deep research, secure systems design, and prototyping, applied across product lines with minimal direction and broad latitude to set the technical agenda. You will set platform-wide direction for securing the AI development lifecycle, define the security architecture, agents, and models operating within it, and represent our AI security point of view externally. You'll be a technical authority other engineers and other teams look to on emerging AI-specific threats, working in close partnership with the AI security research team - drawing on their findings and feeding real-world attack surfaces back into their agenda.
What you'll do:
Own the security strategy and architecture for our Agentic AI ecosystem, LLMs, and models across product lines
Lead threat modeling for the most complex and novel GenAI/agentic surfaces: cross-agent autonomy, multi-agent tool orchestration, and emerging attack classes not yet standardised in the industry
Set the architectural standard for securing model/RAG/data pipelines platform-wide, including access-control and data-boundary models that other teams are expected to build against
Define what "secure-by-design" means for agentic systems, and drive adoption across engineering orgs
Represent our AI security posture externally (standards bodies, industry publications, conferences) and internally to executive stakeholders
Mentor and technically guide engineers on AI-specific security work

we are looking for a Lead Endpoint Researcher.
What You'll Do
Research AI coding agent runtimes, reverse-engineer how tools such as Claude Code, Cursor, Copilot CLI, Codex, and emerging agents execute commands, access files, spawn processes, and interact with the operating system.
Design agent sandboxing architectures that safely constrain filesystem access, networking, process execution, credentials, and other sensitive endpoint capabilities.
Explore and implement OS-native isolation mechanisms across macOS, Windows, and Linux, including containers, namespaces, sandbox profiles, virtualization, restricted processes, and permission models.
Build endpoint "harnesses" that allow TokenShift to intercept, control, observe, and modify agent execution flows.
Design infrastructure for deploying and running local LLMs across heterogeneous endpoint fleets, accounting for operating system, CPU architecture, GPU availability, memory constraints, and hardware acceleration.
Evaluate local inference runtimes and model formats such as llama.cpp, MLX, ONNX Runtime, Ollama, vLLM-compatible runtimes, and emerging alternatives.
Develop mechanisms for model discovery, installation, versioning, updates, health monitoring, and lifecycle management across large numbers of developer machines.
Research hardware-aware model selection and routing, deciding which models can run effectively on Apple Silicon, NVIDIA GPUs, Windows workstations, Linux machines, CPU-only environments, and other endpoint configurations.
Build benchmarks that measure model latency, throughput, memory consumption, startup time, resource contention, and developer experience on real endpoint hardware.
Investigate security boundaries between local models, cloud-hosted models, agents, MCP servers, shells, and enterprise resources.
Collaborate with the core CLI and platform teams to take endpoint research from prototype through production.
Track the rapidly evolving AI agent, sandboxing, and local inference ecosystems and proactively identify technologies should support.

As an Adversarial AI Evaluation Scientist, you will review and validate adversarial agent tasks: indirect prompt-injection (IPI), data-exfiltration, tool-misuse, and related attack scenarios to ensure each one is realistic, fair, deterministic, reproducible, and correctly graded. You will decide whether a scenario genuinely demonstrates a model vulnerability or merely an artifact of how the task was built, and you will combine adversarial-security judgment with statistical analysis and LLM-assisted tooling to find quality issues, analyze attack-success patterns, and recommend or automate improvements.
Responsibilities
Review adversarial tasks for realism and threat-model soundness: is the attackers capability plausible, can the attacker actually reach the poisoned content, and is the target system a realistic one?
Assess attack fairness and attribution: confirm the harm is driven by the adversary (not pre-baked into the system prompt, vouched for by a trusted layer, or otherwise rigged), so a careful agent could plausibly pass.
Validate grading correctness: verify that success/failure criteria, reward logic, and deterministic checks detect the intended harm, with no reward hacks, token-collapse, or evadable gates.
Measure and interpret attack success rate (ASR@K) and its variance; verify deterministic, reproducible behavior across repeated executions.
Judge attack quality - novelty vs. known patterns, sophistication, stealth, and coverage across harm types and target surfaces.
Identify anomalies and systemic issues across large sets of tasks (e.g. scenarios that succeed ~100% of the time against every model - a red flag for an unfair task, not a win).
Define validation guidelines, acceptance criteria, quality thresholds, and release gates for adversarial tasks.
Use LLMs and AI models to analyze attack failures, identify root causes, and assist with task or grader fixes.
Build automated validation, regression, and quality-monitoring pipelines for adversarial task sets.
Work closely with red-teamers, researchers, and task authors to improve attack design, fairness, and reliability.

We are seeking a Senior AI Researcher to lead post-training evaluation, red-teaming, and reinforcement learning (RL) gym audits on open-weight models. The ideal candidate will establish rigorous benchmarking methodologies, evaluate large language models (LLMs) against complex threats like Indirect Prompt Injections (IPI), and construct post-training evaluation pipelines that accurately measure realistic frontier-level security capabilities.
Key Responsibilities
RL Post-Training & Benchmarking
Execute post-training runs (e.g. GRPO) using mainstream open-weight generalist models against security-focused RL environments, targeting threat vectors like Indirect Prompt Injection (IPI).
Reward Diagnostics & Trace Analysis - Analyze live loss curves and rollout traces to identify reward hacking, lazy policy convergence, and flawed or over/under-specified verifiers.
Task & Environment Auditing
Review tasks and multi-turn environments (including tool use, web navigation, and computer use) for realism, threat model accuracy, data distribution, and dataset balance.
Performance Reporting (Gym Cards)
Generate comprehensive evaluation cards detailing hill-climbing performance uplift across checkpoints, failure modes, tokens/turns per rollout, and task-level success rates.
Integration & Orchestration
Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings.

We are hiring a hands-on AI expert to shape our intelligent platform, organizing complex real estate data into actionable insights and predictive models that power smarter investment decisions.

Responsibilities
AI/ML Architecture: Design and implement scalable AI systems using models such as LLMs (e.g., OpenAI, Anthropic) and computer vision (e.g., YOLO, CLIP).
MCP Integration: Architect and orchestrate model context flows using MCP or similar frameworks to deliver personalized, context-aware experiences
FinTech Platform Integration: Embed intelligent features into our backend and frontend systems, ensuring seamless UX and robust performance
Hands-On Development: Write production-grade code across Python, TypeScript, and Node.js. Own key modules and guide technical decisions
Team Leadership: Mentor a few developers, set engineering standards, and foster a culture of experimentation and excellence
Data Strategy: Collaborate with product and data teams to define training datasets, feedback loops, and model evaluation pipelines.

Alice is a leading platform for Trust & Safety teams worldwide, combining cutting-edge AI with world-class expertise to protect users from the widest spectrum of online harms and keep customers two steps ahead of bad actors.
We are looking for a Senior GenAI Data Scientist with strong hands-on experience in GenAI red teaming, adversarial testing, machine learning, and large-scale data analysis.
You will develop the data science and ML capabilities behind an automated GenAI red-teaming platform. You will analyze and cluster adversarial prompts, identify emerging attack patterns, build models for automated prompt generation and mutation, and develop data-driven methods for prioritizing and evaluating security risks across different AI models.
We are looking for a hands-on technologist with deep expertise in data science: clustering, scaling, machine learning, and statistical analysis.
Key Responsibilities
Build agents that act as data scientists at scale, reasoning, planning, and executing multi-step workflows to generate and analyze adversarial attacks.
Manage and analyze prompt and response data from multiple sources (red-team campaigns, model evaluations, synthetic attacks); clean, curate, normalize, and label it for analysis.
Analyze large volumes of structured and unstructured data to uncover trends, clusters, and anomalies, such as attack families, jailbreak techniques, and previously unseen patterns.
Develop ML models and predictive algorithms to automate red-teaming.
Build evaluation and monitoring pipelines to measure attack success and track model behavior across models and providers.
Use statistical techniques and experiments to validate findings and ensure accuracy and reproducibility.
Take research to production: build reliable, scalable systems optimized for cost and latency, with solid engineering practices (testing, CI/CD, telemetry).

we are a leading platform for Trust & Safety teams worldwide, leveraging cutting‑edge AI and world‑class expertise to protect users from the widest spectrum of online harms. By continuously collecting and analyzing data, our solutions ensure customers stay two steps ahead of bad actors in an ever‑changing threat landscape.
Were seeking an outstanding Backend Engineer with a Generative AI orientation to join our research team. We are building a new AI Platform from scratch to standardize AI development methods, ensure excellence and efficiency, and deliver robust, scalable, and compliant AI capabilities. This platform will handle GenAI models deployment, prompt management, data filtering, logging, and monitoring, centrally, so individuals can focus on their own business-specific logic.
Key Responsibilities
Build and maintain CI/CD pipelines that support fast, reliable integration and deployment of GenAI systems across complex environments, including orchestrating multiple models/agents and MCP clients/servers.
Develop, deploy and integrate AI solutions and agentic frameworks, creating both command‑line tools and interactive notebooks that support our teams research and workflow automation needs.
Build and manage data pipelines and databases (SQL) for large‑scale data handling, automating data collection and curation (e.g., adversarial prompts for model red-teaming) to support AI training and evaluation.
Integrate research into production by implementing the latest generative AI advancements (including white‑box LLM development), integrating different models and agents, and developing evaluation frameworks (e.g., agentic reasoning tests) as part of our automated workflows.
Integration & Orchestration: Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings
Deploy GenAI models from repositories such as HuggingFace onto our AWS platform, connect them to data pipelines, and automate end‑to‑end processes for prompt generation, labeling, and response evaluation.

we are looking for a AI Data Lead.
As Data Lead, you will own how data gets turned into decisions across the company - and lead the shift from traditional BI into AI-driven BI, powered by agents that surface insight in real time. You'll work closely with one data engineer today, with room to grow the team as scope expands.
You'll define what the data function looks like when AI is embedded in it, not bolted on - from the underlying infrastructure to the dashboards and agents people actually use. The playbook for AI-native BI is still being written, and you'll write a lot of it.
Key Responsibilities
Data Strategy & Ownership:
Own the vision and roadmap for the data function, including the shift from classic BI to agent-based, AI-driven BI
Set priorities in partnership with senior leadership
Work closely with a data engineer today, with the option to grow the team as the function scales
AI-Driven BI & Agents:
Drive adoption of AI across data workflows - from automating analyses to agents answering business questions in real time
Own the semantic and metrics layer so every dashboard and every agent reads the same definitions
Build in a git-based workflow: dashboards and queries live in git, edited through PRs, built with agents
Expose our data to agents over MCP so business questions get answered without waiting for a dashboard
Define where AI speeds things up - and where human judgment still has to hold the line
Infrastructure & Analytics:
Own the data infrastructure: BigQuery, dbt, pipelines, and data reliability
Lead complex analytics work, A/B testing, and impact measurement, hands-on
Define evals and quality metrics for AI features and agents - not just A/B tests on dashboards
Cross-functional Collaboration
Partner with product, marketing, finance, operations, and engineering to define analytical needs and turn them into solutions
Represent the data function to the organization - senior-level storytelling with data