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At our company, we redefine cyber defense vision by combining AI and human expertise to create products that protect nations and critical infrastructure. This is more than a job; its a Dream job. we are where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Lets build something extraordinary together.
our company's AI cybersecurity platform applies a new, out-of-the-ordinary, multi-layered approach, covering endless and evolving security challenges across the entire infrastructure of the most critical and sensitive networks. Built as part of a broader sovereign AI platform, our technology is designed to operate in on-premise, private cloud, and air-gapped environments, enabling nations to maintain full control over their data, infrastructure, and AI capabilities. Central to our company's proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.
At our company, our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.
Responsibilities
Open Research Tracks
We are hiring across the following specializations. We expect depth in at least one area and the intellectual range to collaborate across them.
Computer Vision: object detection, segmentation, multimodal grounding, vision-language models, contrastive and self-supervised representation learning, low-resource and few-shot visual recognition.
NLP / Speech: LLMs, NERs, relation extraction, span-based and generative IE, semantic textual similarity, multilingual and cross-lingual transfer.
Reinforcement Learning: MDPs, POMDPs, model-based and model-free RL, Online Offline methods, reward modeling, sim-to-real transfer, compute-aware planning.
Graph Learning: GNNs, graph clustering, community structure, generative methods, knowledge graph embeddings, dense and sparse semantic retrieval.
Optimization: convex and nonconvex optimization, constrained and Lagrangian methods, combinatorial and integer programming, knowledge distillation (response, feature, and relation-based), test-time optimization, Bayesian optimization, resource-aware inference.
Representation Learning: contrastive learning, self-supervised and unsupervised pre-training, disentangled representations, metric learning and embedding spaces, cross-modal and multimodal alignment, meta learning (hypernetworks), transfer learning and domain adaptation, probing and interpretability of learned representations, world models.
Neurosymbolic AI: neuro-symbolic integration, differentiable theorem proving, inductive logic programming (ILP), probabilistic soft logic (PSL), causal inference and structural causal models (SCMs), programmatic and compositional reasoning
Responsibilities:
Define and execute a research within your track, experiments, quality gates, and upper bounds in rigorous manner.
Collaborate across tracks on system integration and cross-disciplinary research.
Collaborate work with engineering teams until production.
Mentor junior researchers and contribute to a culture of technical excellence.

At our company, we redefine cyber defense vision by combining AI and human expertise to create products that protect nations and critical infrastructure. This is more than a job; its a Dream job. we are where we tackle real-world challenges, redefine AI and security, and make the digital world safer. Lets build something extraordinary together.
our company's AI cybersecurity platform applies a new, out-of-the-ordinary, multi-layered approach, covering endless and evolving security challenges across the entire infrastructure of the most critical and sensitive networks. Built as part of a broader sovereign AI platform, our technology is designed to operate in on-premise, private cloud, and air-gapped environments, enabling nations to maintain full control over their data, infrastructure, and AI capabilities. Central to our company's proprietary Cyber Language Models are innovative technologies that provide contextual intelligence for the future of cybersecurity.
At our company, our talented team, driven by passion, expertise, and innovative minds, inspires us daily. We are not just dreamers, we are dream-makers.
The Responsibilities
Open Research Tracks
Familiarity with at least one is expected:
Computer Vision: object detection, segmentation, multimodal grounding, vision-language models, contrastive and self-supervised representation learning, low-resource and few-shot visual recognition.
NLP / Speech: LLMs, NERs, relation extraction, span-based and generative IE, semantic textual similarity, multilingual and cross-lingual transfer.
Reinforcement Learning: MDPs, POMDPs, model-based and model-free RL, Online Offline methods, reward modeling, sim-to-real transfer, compute-aware planning.
Graph Learning: GNNs, graph clustering, community structure, generative methods, knowledge graph embeddings, dense and sparse semantic retrieval.
Optimization: convex and nonconvex optimization, constrained and Lagrangian methods, combinatorial and integer programming, knowledge distillation (response, feature, and relation-based), test-time optimization, Bayesian optimization, resource-aware inference.
Representation Learning: contrastive learning, self-supervised and unsupervised pre-training, disentangled representations, metric learning and embedding spaces, cross-modal and multimodal alignment, meta learning (hypernetworks), transfer learning and domain adaptation, probing and interpretability of learned representations, world models.
Neurosymbolic AI: neuro-symbolic integration, differentiable theorem proving, inductive logic programming (ILP), probabilistic soft logic (PSL), causal inference and structural causal models (SCMs), programmatic and compositional reasoning
Responsibilities:
Train and evaluate models across research tracks, iterating fast while documenting rigorously.
Build and maintain benchmarking pipelines and evaluation suites.
Curate, structure, and preprocess datasets; contribute to synthetic data generation workflows.
Run ablations and controlled experiments to support research hypotheses.
Reproduce and stress-test results from recent literature relevant to the group's work.
Collaborate across tracks and with engineering teams through to production handoff.

we are looking for a AI/ML Applied Researcher.
As AI Applied Scientist, youll be at the intersection of advanced technology and meaningful impact with clear business cases. You will have access to real-time (RT) big data from multiple Sensors & Data sources, and a chance to discover, explore, research, and develop cutting-edge solutions that directly impact industrial manufacturers around the globe. As part of the Applied AI/ML Science team, you will have the opportunity to continuously grow and learn while building and deploying advanced models directly into our products, and stay at the forefront of the technologies in the world, witnessing firsthand their impact on our customers.
A Day In Your Life:
Own the algorithm lifecycle from problem definition and data analysis to prototyping and delivering production-ready models.
Research, design, and build anomaly classification, predictive, insights, recommendations, and other types of models from various data sources, including sensor time-series data, textual data, images, and more.
You will Ensemble multi-modalities by exploiting and enriching the most from our data collections platform and feature extraction and store phases, using classic statistical methods, deep learning, Transformers, Graphs, Foundations Modules (as TSFM, Graph Transformer, TGNN), and any available technology to extract recommendations for taking real-world, life-changing actions.
Engage with customers and collaborate with our product team to develop innovative solutions utilizing new types of data.
Work with cloud-based big data platforms for training, distributed processing & experiments tracking (e.g., Metaflow, Grafana, Outerbounds, Databricks).
Partnering and contributing to modern technologies: LLMs and agents (GPT, Claude, Gemini), defining & building Gurdrails, reasoning chain as function call, planning, ML-based vectorization and embeddings, and stream analysis.
As part of our hybrid working policy, you will be expected to work from the office at least twice weekly, which includes at least one day in Haifa.

We are looking for a Senior AI Researcher to join Immunais foundation models team and help advance large-scale models for single-cell RNA (scRNA) data.

In this role, you will focus on training and improving transformer-based and deep learning models, developing algorithmic solutions to challenging modeling problems, and adapting modern foundation model techniques to biological data. This is a hands-on research position for someone who enjoys building and training deep models, understanding their behavior, and pushing them to perform better.

You do not need prior biology experience - but you should be excited to apply your deep learning expertise to a new scientific domain.

Location: Ramat Gan (hybrid model).

What will you do?
Develop, train, and refine transformer-based and related architectures for large-scale scRNA data.
Tackle challenging modeling problems with thoughtful, principled approaches.
Apply ideas from NLP and vision (pretraining, self-supervision, transfer learning) to biological data.
Plan careful experiments, perform ablations, and deeply analyze results to guide model improvements.
Engage directly with large, noisy biological datasets and build intuition for model behavior in this setting.
Partner with engineers, data scientists, and biology experts to turn research ideas into working systems.

We are looking for a Senior AI Researcher to work closely with our delivery team, translating real-world clinical and scientific questions into rigorous AI and machine learning solutions. In this role, you will lead the development of methodologies, models, and benchmarking frameworks that address complex clinical problems using our unique immune-cell data. Your work will directly impact how our partners understand biology, evaluate hypotheses, and make decisions.

Location: Ramat Gan (hybrid model).

What will you do?
Partner with the collaborations team and scientists to frame complex clinical and biological questions as well-defined data science and modeling problems.
Design, implement, and evaluate the right analytical and modeling approaches for each problem, including robust benchmarking and validation strategies.
Clearly communicate results, limitations, and tradeoffs to internal teams and external partners, enabling confident, data-driven decisions.
Establish best practices for rigor, reproducibility, evaluation, and documentation across delivery-focused data science work.
Work hand-in-hand with immunologists, computational biologists, AI scientists, and engineers to ensure solutions are scientifically sound and production-aware.
Translate modeling solutions into engineered and iterable products used by the internal collaborations team.

Senior AI Research Engineers at our company develop algorithmic solutions that power new innovative features and continuously improve existing ones, with a strong emphasis on training AI / ML models as a core part of the role. The work spans a wide range of domains - from training generative AI and LLM-based models to create musical content at scale for our Piano, through building deep learning computer vision models that enhance user drawings in our Draw, to developing real-time audio ML models for music transcription that elevate feedback in our Guitar.

You will take full end-to-end ownership of cutting-edge algorithms and AI models - leading the process from ideation and research, through development, to production-grade implementation and engineering within our products. This includes not only designing and building AI models, but also ensuring robust, efficient, and scalable integration into the app - directly shaping the experience of millions of users.

Join the group that builds the foundation of our perception stack. We are looking for a key member to join our Visual GenAI team, dedicated to advancing state-of-the-art research in visual synthesis and Diffusion Models within a high-impact, real-world AV environment.
What will your job look like:
Own the full development lifecycle: from training generative models to adapting existing state-of-the-art architecture for concrete, real-world autonomous driving use cases.
Research, evaluate, and improve GenAI and computer vision models with a focus on scalable and clean Python implementation.
Collaborate with cross-functional teams, including AI researchers and perception teams, to integrate synthetic data into real-world AI applications.
Stay up to date with the latest advancements in AI, Generative Models, and Computer Vision.

Required Data Researcher
Israel - Tel Aviv
About the role:
As a Data Researcher, you will own one of the companys most critical assets: the accuracy, robustness, and performance of what is widely recognized as the leading e commerce fraud decisioning system in the industry.
In this role, you are expected to think creatively and push boundaries. You will design smart logical and technical solutions to real and constantly evolving business challenges faced by some of the worlds largest and most sophisticated online retailers. Your work will operate at the intersection of deep data research, production systems, and real world impact.
You will also have exposure to key customers, working closely with them to deliver analytical insights and adapt solutions to their unique needs. This direct connection between research, product, and customer impact is a core part of the role.
You will join a high caliber, fast moving research group with a strong culture of excellence, curiosity, and ownership. We work in an informal, collaborative environment where strong opinions are welcome and great ideas move fast. The role offers meaningful growth opportunities, with clear paths toward both senior individual contributor and managerial tracks, depending on where you want to take your career.
Your work will directly shape our product, protect millions of consumers and merchants, and help define the future of trust in digital commerce.
What you'll be doing:
Invent, design, implement, and continuously refine the core decisioning logic and models that run in our live production systems
Research complex fraud patterns, adversarial behaviors, and emerging global threats at scale
Leverage rich and diverse datasets to generate actionable insights, build new system components, and advance feature engineering efforts
Develop, prototype, and automate tools and processes that improve the accuracy, scalability, and efficiency of our systems
Collaborate closely with Data Scientists, Analysts, Researchers, and Engineers to build the next generation of our AI driven technology.

We are looking for an exceptional Senior AI Researcher to join our fast-growing company during a pivotal stage of market expansion. In this role, you'll lead groundbreaking research in SOC automation, develop industry-defining benchmarks, create innovative prototypes that transform into product features, and establish thought leadership through conferences and publications. If you're passionate about revolutionizing security operations through AI and excited about shaping industry standards, we'd love to hear from you!
What Will You Do?
Lead research initiatives that redefine SOC automation capabilities, creating industry benchmarks and evaluation frameworks for AI in security operations
Design and develop functional prototypes that demonstrate breakthrough concepts, transitioning research into product features with engineering teams
Collaborate with security researchers, Product Marketing, and the Head of AI to analyze competitors and align research priorities with commercial objectives
Present findings at premier conferences (BlackHat, RSA) and publish tools/content that advance security automation and establish thought leadership
Mentor engineering teams on advanced AI techniques and their practical applications in security contexts

We are looking for an AI (vision) Researcher.

As part of the team you will:

Develop novel solutions for retinal screening, spearheading healthcare accessibility.

Help prevent blindness worldwide .

Join a leading company with unique FDA approved products.

Learn from top ML researchers in the field.

We are seeking a Senior AI Researcher Reporting to CEO . This role is pivotal, aimed at addressing some of the AI industry's most complex challenges. Senior AI Researcher will act as a versatile 'joker card' within the R&D, stepping in to expedite projects, solve bottlenecks, and provide specialized expertise as needed. This role is designed to minimize latency and accelerate outcomes across various technical domains within the company. The ideal candidate will have strong technical expertise, excellent coding skills, and the ability to solve problems effectively.
The role doesn't include managerial responsibilities but does involve close collaboration with internal teams and the capability to inspire and guide staff. As a professional authority within our teams, the Technology Director will improve quality and speed up problem resolution. Adeptness in analyzing complex issues quickly, switching contexts when needed, and fostering a culture of growth and technical excellence is also desired.

We are seeking a hands-on Applied AI Scientist to join our core R&D team and drive the development of next-generation AI systems for autonomous driving. This role sits at the intersection of applied research and deployment - you will go from reading papers to shipping production systems. You will work directly on our multi-layered autonomy architecture, with a primary focus on real-time predictive models for driving decisions.
A deep technical role for someone who thrives on turning cutting-edge research into real, working systems under hard constraints.
Responsibilities:
Own the research-to-deployment cycle for predictive driving models - from literature review and prototyping through to production integration
Design, implement, and iterate on real-time predictive models, including vision-language models, motion prediction models, and inverse reinforcement learning approaches (e.g., imitation learning, reward recovery)
Collaborate on higher-level reasoning systems, contributing to vision-language-action models that handle complex edge cases and long-horizon planning
Bridge cloud-scale training with edge deployment - work on model compression, quantization, speculative decoding, and efficient inference for embedded automotive platforms
Evaluate and integrate state-of-the-art techniques from the broader AI research community into our autonomy stack
Collaborate closely with internal R&D teams to unblock technical challenges, accelerate delivery, and raise the overall technical bar.

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.
What Youll Do:
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 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.