לוח משרות דרושים הייטק AI , לחיפוש זה נמצאו 302 משרות

We are looking for a talented, passionate, and experienced Senior GenAI Engineer to join our team. You will design and build production-grade GenAI applications from the ground up, using technologies such as Google ADK, Docker, and AWS (Bedrock, AgentCore etc). Were looking for someone with a genuine passion for GenAI, strong software engineering skills, hands-on experience developing GenAI solutions, and a collaborative, hands-on approach. You will also work closely with the teams researchers, helping transform research code and prototypes into reliable, secure, and scalable containerized applications that can be deployed and operated in the cloud.
Main Responsibilities:
Design and development of production-grade GenAI applications and agentic systems
Build and operate reliable, secure, and scalable AI-powered services, with a focus on performance, observability, and cost
Drive engineering best practices, including testing, evaluation, CI/CD, code review, and technical design, while contributing to product and technical innovation
Work closely with the teams researchers to transform research code and prototypes into reliable, containerized applications that can be deployed and operated in the cloud

Required Research Engineer, Generative Media
About the job
Research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
The Creative Camera team mission is to imagine and build the future of photography and videography. Our team is reinventing digital imagery: from pioneering new generative and diffusion models for creating the highest-quality images and videos possible, to creating entirely new ways of capturing and reliving our experiences.
The Platforms and Devices team encompasses our various computing software platforms across environments (desktop, mobile, applications), as well as our first party devices and services that combine the best of AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user's interaction with computing faster and more seamless, building innovative experiences for our users around the world.
Responsibilities
Design, implement, train, and evaluate deep learning architectures-particularly diffusion models and transformers-for image and video synthesis.
Develop and apply algorithms in computer vision, graphics, and machine learning for our applications, focusing on computational photography, computational videography, and content creation.
Conduct both fundamental and applied research to identify and solve creative, novel problems that revolutionize editing, and creation of visual media.
Leverage deep theoretical and practical knowledge to prototype new technologies that unlock visual experiences for users.
Build upon our strong track record of publishing graphics venues (with previous publications including Dreambooth, GameNGen, ObjectMate, LightLab, and many more), and successfully transfer these innovations into our products that reach millions to billions of users.

We're looking for a Senior Applied AI Scientist to sit at the frontier of AI security - turning emerging threats into the detection models that protect how AI is used inside the world's largest organizations. You'll be part of the AI Security Research department, working hand-in-hand with security researchers to translate threat intelligence into trainable signals that catch malicious behavior and security risks across the AI-powered workflows of Fortune 500 companies.

You'll bring deep technical versatility - reaching for classical ML, deep learning, or agentic based approaches based on what the problem demands, and the evaluation rigor to know when a model is truly ready for the real world. If you want to define what AI security engineering looks like, not just practice it, this role is for you.

Responsibilities:
Build, train, and ship detection models end-to-end, from raw data to production.
Choose the right method for each problem - traditional ML, deep learning, fine-tuned LLMs, agents or heuristics - based on theoretical insights turned into practical results.
Partner with security researchers to turn security research outputs and domain expertise into detection capabilities.
Own evaluation: design benchmarks, build labeled datasets, and define production standards.
Monitor models in production across all paradigms - ML, deep learning, LLM-based, and agentic systems to track degradation and ensure reliability.
Iterate fast, with a tight feedback loop between model performance and product outcomes.

This is a research‑first role focused on deeply understanding LLM internals to improve the security of AI agents. Youll design careful experiments on activations and interpretable features- e.g., probing, attribution & ablation/patching, representation‑geometry analyses-to uncover mechanisms behind jailbreak, indirect prompt injection, and other attacks. Then translate those insights into signals that can be used for detection and analysis of a model response.

The field of LLM interpretability at scale is exploding, with several major publications in the last months, and major opportunities for innovation.

Responsibilities:
Investigate model internals, including activation/features analysis, unsupervised clustering, discovery of directions in latent space, etc. It may also require training specific model parts to improve interpretability metrics.
Design security‑grounded evaluations: curate datasets for different attack types, evaluate performance of different white box (model internals) methods compared to black box (input/output only) baselines.
Publish and share: produce our Labs posts and open artifacts; when the work is strong, aim for tier‑1 ML venues (NeurIPS, ICML, etc.) and security forums. A publication of code and/or trained models in cases of community relevant novelty.
Build tools: Several open source libraries exist (like Anthropics attribution graphs infra), but the research in the field is very dynamic, which will require you to build and adapt tools to your own research directions. This also includes agents to automate research work and distill knowledge from designed experiments.

Required Research Engineer, Generative Media, DeepMind
About the job
Our team's mission is to build frontier generative media experiences. We are pushing the boundaries of what is possible by developing real-time, interactive video models like Genie, as well as exciting new applications and capabilities.
We have a proven track record of researching and delivering foundational generative models for video and action-controllable virtual worlds, as well as applications such as video stylization, reference-to-video generation, and precise object insertion and removal.
Artificial intelligence will be one of humanitys most transformative inventions. At DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Responsibilities
Design, implement, train, and evaluate novel deep learning models for real-time generative media, including video-audio synthesis, manipulation, and understanding.
Develop and optimize algorithms and systems for AI experiences on Omni platforms, focusing on live dialog, live video editing, and crosscut.
Collaborate with research scientists and engineers to prototype, experiment, and scale generative AI techniques using software engineering best practices.
Contribute to the design of datasets, evaluation methodologies, and infrastructure required for training and testing generative models.
Analyze results to iterate on architectures, contribute to research publications, and help deploy successful research into GDM and our products.

Our data & AI Division is looking for data Engineer to join our team! We are looking for a data & AI Engineer to join our growing team and help design, build, and deliver modern data & AI solutions for enterprise customers. Full-time | Central Israel?Key Responsibilities

* Design, build, and maintain scalable data Pipelines and data Platforms
* Develop ETL /ELT processes and cloud-based data solutions
* Work with structured and unstructured data from multiple sources
* Build AI-powered solutions including AI Agents, RAG pipelines, and LLM-based applications
* Integrate AI and data solutions with enterprise systems using APIs and cloud services
* Develop data models, data warehouses, and lakehouse architectures
* Collaborate with data Scientists, BI teams, and business stakeholders
* Monitor, optimize, and troubleshoot production environments

we are the agentic intelligence platform for cyber and quality teams. We protect and analyze data from millions of connected assets worldwide, including vehicles, robots, charging stations, fleets, and the broader physical-AI ecosystem - for Fortune 500 companies. Our platform spans AI and API security, cybersecurity XDR, proactive quality detection, and cyber threat intelligence..
Runtime AI & API security is our fastest-moving frontier: securing AI agents, APIs, and MCPs in enterprise environments where autonomous systems now act on real-world infrastructure. we are uniquely positioned to lead the segment, focusing on the intersection of AI agents and physical assets, an angle no other vendor can claim.
We are building this into a fast-growth business line. You will lead our company's AI & API Security business globally - the number, the sales strategy, and the execution. This is a builder's role, and you will define and lead the sales motion, work closely with our regional sales leaders, marketing, product, and CSM teams, close the first flagship logos yourself, and then scale it through our global sales organization.
This role is full-time and is based in Herzliya, Israel.
Responsibilities
Lead the AI & API Security business - carry a global revenue target, and own pipeline, forecast, and pricing for the line of business.
Build and lead the AI & API Security sales motion - define ICP, segmentation, packaging, and the sales playbook, in partnership with regional sales leaders, product, marketing, CSM, and cyber services.
Sell directly - personally lead strategic enterprise outreach end to end, from first meeting through technical validation, solution review, procurement, and close.
Enable the global sales teams - arm regional sellers in North America, EMEA and APAC with training, playbooks, and deal support so AI & API Security scales beyond your own quota. Support the sellers in every stage of the deal and push for closing the deals.
Act as the subject matter expert of the offering - run discovery, architecture discussions, demos, and POVs; represent our company at customer executive briefings, industry events, and analyst conversations.

This is a role for someone who wants to help build the org of the future, where agents and people work side by side. A strong software engineer who wants to own complete solutions rather than one piece of a larger system. Youll be expected to figure out how to solve the problem and make it work.

What Youll Do
Take a problem from fuzzy to shipped - work from a rough definition and a priority, and figure out the rest.
Architect, build, and ship AI agents, applications, and intelligent workflows end-to-end. Integrate AI solutions with company data, APIs, and internal systems.
Rapidly prototype and experiment with new AI technologies, turning successful concepts into reliable production solutions.
Embed with internal users - sit with the team, watch how they work, map the process, build what fixes the highest-leverage part.
Own the build end-to-end - architecture, implementation, evals, logging, deployment.
Debug the non-deterministic - when a system fails on edge cases, you build the evals and guardrails to catch it rather than reading logs by hand.
Stay honest about what AI can and cant do - scope realistically, ship reliably, dont overpromise.

we are looking for a VP of Product Management to lead our Identity Security and AI Security product teams and shape how Tenable One addresses the emergence of agentic risk across the entire platform.
You Will:

Lead the Product Evolution. Take direct ownership of Identity Security and AI Security product teams - deciding, deliberately, where one domain's remit ends and the other begins as the line between "identity" and "agentic AI" keeps blurring for our customers.

Define the Strategy Before the Market Does. Build and pressure-test the hypotheses that will shape how Tenable - and the industry - thinks about agentic and non-human identity risk, then turn that into a roadmap and quarterly execution with engineering.

Own the Narrative at the Top. Represent this strategy to most senior leadership, including our CEOs, and to sales, field, and marketing - deciding when to lead with confidence and when to flag risk honestly.

Lead Without Borders. Build the kind of close, high-trust relationship with a distributed team - including a France-based PM - that most leaders only manage by accident of geography.

Reach Beyond This Scope. Take on the cross-functional, company-wide strategic work this platform-level view naturally creates - this role is intentionally bigger than any one product line.

Join the company and help scale AI capabilities across the organization. As the AI IT Centre of Excellence (COE) Deploy Lead, you will own the methodologies, standards, and deployment practices that make AI implementation repeatable and reliable across Strauss.
You will act as the professional focal point for AI delivery - working alongside AI experts, data teams, software developers, architects, and business stakeholders to ensure consistent, high-quality delivery of AI initiatives while building organizational knowledge and capability.
Employment model: Israel-based, offshore/outsourcing engagement (not a buy-out).
Key Responsibilities
Lead the deployment methodology, standards, and best practices of the AI IT Centre of Excellence (COE).
Guide and support development teams in the design, implementation, and adoption of AI solutions.
Serve as the professional focal point for AI delivery frameworks, tools, architectures, and implementation approaches across the organization.
Facilitate collaboration between AI experts, data teams, software development teams, architects, and technology stakeholders.
Provide training, coaching, and ongoing enablement for teams delivering AI initiatives.
Build and maintain AI playbooks, implementation guidelines, reference architectures, templates, and knowledge assets.
Drive consistency, quality, governance, and scalability across AI implementations.
Support project execution, manage dependencies, identify risks, and help teams overcome implementation challenges.
Lead AI knowledge-sharing activities, communities of practice, and professional development programs.

This role sits at the intersection of AI and high-performance systems engineering, focused on solving real-world problems under strict constraints. You will work on systems where performance and reliability are critical and where improvements have a direct, measurable impact on real-world safety.

This is a senior, systems-focused role with end-to-end ownership over performance and reliability of production computer vision pipelines. You will define optimization strategies, identify bottlenecks across the system, and drive improvements under real-world constraints.

What youll do
Build and optimize real-time computer vision pipelines running on edge systems processing live maritime video streams (e.g, NVIDIA Jetson, Triton Inference Server).
Take models from research and turn them into production-ready, reliable components deployed on vessels.
Profile and improve end-to-end system performance across: multi-camera video ingestion; preprocessing; inference; postprocessing.
Identify and resolve bottlenecks across CPU, GPU, memory, and pipeline coordination.
Make and justify tradeoffs between latency, accuracy, stability, and resource utilization.
Design and implement robust data and inference pipelines (video -> model -> actionable output for crew).
Develop benchmarking and evaluation workflows to measure performance end-to-end and support release gating.
Build and improve observability tools, including logging, monitoring, and debugging workflows for production systems.
Define and maintain clear interfaces between research code and production systems.
Work closely with research and backend teams to integrate new models into production systems.
Continuously improve system efficiency and reliability under hardware and runtime constraints.

Required Compilation Technology Expert
Job Description
We are forming a new team to explore AI Compilation Boundaries
This team will operate under a unique manner, as described below -
The environment
- Startup-like intensity
Small team
High ownership
High visibility
Merit is the essence, not titles
- Fast pace
Todays state of the art is tomorrows baseline
We aim to go beyond
- Incubator mindset
Take on hard challenges, dismantle, and regroup as novelty
- Elite technical culture
Strong experts
Strong standards
Strong execution.
Ratio beats seniority, not the other way around
- No artificial boundaries
We challenge assumptions, and question constraints, and leave no quarter
- Direct exposure to the latest and greatest AI technology
Real hardware
Real toolchains
Real large-scale AI applications
All pass through and impacted by our work
- First-principles thinking
out-of-the-box is a compromise
Do not accept that a box had to exist in the first place.

Required Research Intern: Next-generation Computer Architecture for Racks and SuperPods
Responsibilities:
The research focuses on designing next-generation rack and SuperPod architectures that combine elasticity through resource pooling with optimal performance enabled by holistic co-design across applications, parallel programming models, interconnect fabrics, and compute, memory, and switch chip architectures. On the communication side, the work architects scalable, high-bandwidth, and low-latency scale-up fabrics across multiple chips at rack and SuperPod scale. On the memory side, it explores richer protocol semantics beyond traditional load/store operations to reduce unnecessary data movement and rethinks memory hierarchies to expose large-scale capacity with near-local latency. On the compute side, it analyzes the requirements of General Compute and Generative AI workloads and their parallel programming models to fully leverage large-scale system resources.
Investigate and prototype new architectural features, including but not limited to:
o Tiered Memory: Explore advanced memory organizations, including hardware-managed caching, to enable software-transparent, fine-grained promotion and demotion of cache lines across the memory hierarchy.
o Prefetching/Speculation: Evaluate and design existing and next-generation hardware prefetching and speculation mechanisms to effectively hide local and remote memory latencies at rack and SuperPod scale.
o Near-Memory/Network Processing: Develop support for key primitives executed at the memory and network layers to minimize unnecessary data movement across sparse, dense, and pointer-based data structures.
o Workload-Centric Co-Design: Study optimal parallelization strategies at rack and SuperPod scale for both General Compute and Generative AI workloads, and design dedicated hardware support for widely used parallel programming primitives such as RPCs and collective communication.
Write reports and papers on the research results and present them.