לוח משרות דרושים הייטק AI  בתל אביב יפו, לחיפוש זה נמצאו 154 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.

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.

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.

This role reports to the AI Team Lead and is based in Tel Aviv (hybrid model).

You'll own our AI reference architecture end to end - the patterns, standards, and platform capabilities that every AI initiative across engineering builds on. You'll lead architecture reviews, set the technology roadmap, and make the call on what we adopt, pilot, or retire.

Be part of our AI architecture design and ensure every AI initiative aligns with it.
Define reusable patterns for AI services, agents, retrieval systems, evaluation pipelines, and infrastructure.
Evaluate emerging technologies, vendors, and foundation models, and maintain a forward-looking roadmap.
Partner with Product leadership during early discovery on AI-intensive initiatives.

we are looking for a AI Transformation Engineer.
You'll join the AI Platform team, the group building the AI infrastructure, agentic workflows, and tooling that power R&D organization. Our mandate is to make the entire software development lifecycle faster and smarter with AI, and we're a small, hands-on team that ships real systems, not pilots
This isn't a role where work arrives as tickets, and it isn't a prompt-engineering role. You'll own a set of KPIs the team is driving, and it's on you to turn a fuzzy goal into something measurable: figure out what actually needs to be built, define how you'll know it's working, and build it. That means talking to engineers, understanding where the SDLC breaks down, and turning that into agentic workflows and platform capability that ship and hold up over time.

We are seeking an innovative AI Engineer to join our team in building the next generation of security protection and cyber attack detection for endpoints. In this role, you will leverage cutting-edge AI technologies and advanced agentic approaches to revolutionize how cyber threats are identified, analyzed, and mitigated. You will design, build, and optimize intelligent AI agents that analyze complex security data to deliver highly accurate, automated verdict decisions on malicious activities, ensuring endpoint resilience against sophisticated attacks.
Key Responsibilities
Agentic Framework Design: Design and implement intelligent, multi-agent AI systems capable of executing complex security analysis workflows and automated decision-making.
Context Optimization: Analyze diverse security datasets to determine the most effective context, telemetry data, and threat intelligence to feed into AI agents to maximize decision accuracy and minimize false positives.
Model Configuration & Integration: Evaluate, configure, and deploy a variety of state-of-the-art language models and AI architectures tailored for high-performance security use cases.
Data Analysis & Performance Evaluation: Perform deep data analysis on security events and model outputs to continuously refine agent logic, benchmark performance, and optimize AI-driven detection mechanisms.
Collaboration & Deployment: Partner with security research and engineering teams to integrate intelligent AI components into production systems seamlessly and securely.

We are looking for an R&D Solutions Engineer to join the Agentic AI team within our companys AI for Security organization. This is a customer-facing role embedded directly within R&D, with a dotted-line relationship to the broader Solutions Engineering organization. You will work closely with R&D, Product, Sales, and SE teams to bring new Agentic AI capabilities into the hands of customers early, learn from real-world usage, and help turn emerging capabilities into products and sales motions that can scale.
You will report directly to the Director of Agentic AI within the AI for Security organization.
This is not a traditional presales SE role. Your primary focus will be building and managing relationships with design partners and Early Access (EA) customers, driving them toward meaningful product usage, and creating a tight feedback loop between customers and the teams building the product. You will also play a key role in taking capabilities that initially require direct R&D involvement and making them repeatable for the broader our company SE organization.
Key Responsibilities
What You'll Do
Own Design Partners and Early Access Customers
Identify and recruit strong candidates for design partnerships and Early Access programs, working closely with Sales and SE teams.
Own the technical relationship with design partners and EA customers from initial engagement through deployment and ongoing usage.
Build and maintain a healthy portfolio of actively engaged design partners.
Understand customers environments, workflows, security requirements, and success criteria.
Help customers deploy, configure, integrate, and evaluate emerging Agentic AI capabilities.
Drive customers toward meaningful real-world usage rather than isolated demos or proofs of concept.
Track adoption, outcomes, blockers, and feedback across the design partner and EA portfolio.
Maintain ongoing relationships with customers to ensure early enthusiasm turns into sustained engagement and actionable product feedback.
Bring the Field into R&D
Develop a deep understanding of how customers use the product and where they encounter friction.
Translate customer conversations into concrete product requirements, technical insights, and prioritization signals for R&D and Product.
Identify recurring patterns across customers rather than treating every request as a one-off.
Work directly with engineers and product leaders to reproduce issues, validate assumptions, and test potential solutions.
Regularly synthesize customer feedback into clear recommendations for R&D and Product.
Help the R&D organization understand what is required for new capabilities to succeed in real production environments.
Make New Capabilities Sellable and Repeatable.

We are looking for an exceptional researcher at the intersection of artificial intelligence and cybersecurity. This role is intended for someone who combines deep security expertise with a practical understanding of modern AI systems and the curiosity to explore how they can be attacked, abused, and defended.
Key Responsibilities
Research vulnerabilities, attack techniques, and emerging security risks in AI models, agents, applications, and supporting infrastructure.
Develop innovative attacks, proofs of concept, evaluation methods, and defensive solutions.
Analyze how model behavior changes across problem domains, task structures, prompts, tools, resources, harnesses, and model architectures.
Translate research findings into clear security requirements, technical concepts, publications, and practical protections.
Present original research at leading international cybersecurity conferences.
Maintain a strong understanding of developments across the AI and cybersecurity landscapes.
Collaborate with security researchers, AI engineers, product teams, and other stakeholders to turn research into measurable impact.

A Senior AI/ML Engineer role on our Data team, sitting at the intersection of AI engineering, data science, and data platform. You'll build agentic, LLM-based systems that turn vast amounts of unstructured information into high-quality structured signals - powering our products and surfacing legal risk - owning each solution across the full lifecycle, from exploration to production. You'll work closely with data engineers, data scientists, product managers, and legal experts to turn ambiguous problems into scalable AI capabilities. This is a hands-on role for someone with strong engineering judgment, deep data/ML expertise, and a focus on reliability beyond the prototype stage.

What You'll Do

Design, build, deploy, and operate production AI systems: agentic, LLM-based extraction workflows and batch/online inference pipelines and owning them from exploration through deployment, monitoring, and iteration.
Define quality metrics and build evaluation datasets, testing processes, and feedback loops for ML and LLM systems; monitor accuracy, latency, cost, and data quality, and investigate and fix failures.
Explore large, complex datasets to surface useful features and patterns, and translate findings into production-grade implementations.
Partner with data engineers on ingestion, transformation, orchestration, and storage to deliver dependable data products.
Use AI-assisted coding tools (e.g., Claude) effectively while maintaining strong technical judgment and ownership of your systems.
Help shape engineering standards for production AI: testing, observability, reproducibility, data lineage, versioning, and safe deployment.