לוח משרות דרושים חוקר בינה מלאכותית משרה מלאה, לחיפוש זה נמצאו 21 משרות

Our breakthrough World Model technology combines aerial imagery with real-time ground views to build an accurate, rich representation of the vehicle's environment. As part of our World Modeling team, you will drive the next generation of AI-powered, cloud-defined vehicle technology-tackling ambitious research and turning cutting-edge deep learning into real-world solutions.

Responsibilities
Research, design, and implement state-of-the-art deep learning models in computer vision (e.g., detection, segmentation, transformers, self-supervised learning).
Translate research into production-ready algorithms, writing clean, efficient, and scalable code in Pytorch.
Explore creative ideas, experiment with novel architectures, and continuously push beyond current SOTA.
Take ownership of your work end-to-end, from concept through prototyping to deployment in production.

We are seeking a highly motivated AI Researcher to join our core Data Science team. In this role, you will be at the forefront of tackling complex, high-impact business challenges by leveraging cutting-edge GenAI technologies, advanced AI research, and classical machine learning and statistical modeling.
You will not just be building models; you will be driving research from ideation to production. We are looking for a true self-learner, someone who thrives in an environment that demands both a strong sense of ownership over your deliverables and deep collaboration. You will work closely with fellow researchers, product managers, and software developers to translate abstract business problems into scalable data-driven solutions.
If you are passionate about staying ahead of the AI curve, building agentic workflows, and proactively driving your research initiatives forward within a collaborative team structure, this is the perfect role for you.
What You Will Do:
End-to-End Research & Ownership: Lead targeted research projects from initial hypothesis through to production-ready solutions. Take strong ownership of your work's performance and partner with engineering to ensure successful, scalable integration
Applied AI Research & Integration: Lead research into LLMs, agentic systems, and modern AI methods, then design and build the workflows that bring them into our ecosystem.
LLM Evaluation & Optimization: Contribute to and establish rigorous evaluation frameworks for LLMs to ensure accuracy, safety, and business alignment in practical applications.
Cross-Functional Collaboration: Act as a bridge between data, engineering, and product. Work with Developers to ensure seamless integration of ML features into the core product.
Continuous Innovation: Act as an internal advocate for emerging AI research and methodologies. Continuously read, experiment, and implement state-of-the-art techniques to advance our research agenda and accelerate research velocity.

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.

At our company, our purpose is to make the world a safer place by protecting the integrity of the global financial system. We do this by putting AI at the core of both our technology and our way of working. Our AI-driven solutions help banks and fintech companies worldwide detect and stop serious financial crime, from human trafficking and terrorist financing to sophisticated money laundering, while advanced technology, automation, and AI-driven tools help our teams collaborate smarter, move faster, and continuously improve how we build, deliver, and innovate.
About the role: We are looking for an Algorithm Researcher to turn expertise, initiative, and bold thinking into real impact on the next generation of AI-driven financial crime detection.
If you combine strong mathematical and research capabilities with advanced AI expertise, and if you are motivated by turning complex theoretical concepts into scalable, production-ready algorithms that solve real-world financial crime challenges, we could be your next challenge.

?Responsibilities:
* Designing and developing advanced algorithms for complex financial crime detection challenges
* Translating mathematical models and research concepts into scalable production systems
* Building and optimizing ML and LLM-based solutions for real-world deployment
* Working with transformers, attention mechanisms, sequence modeling, and representation learning
* Developing solutions using RAG, embedding models, vector databases, and generative AI evaluation frameworks
* Designing AI agents, tool-using LLM architectures, and autonomous decision-making pipelines
* Improving model accuracy, robustness, explainability, and inference efficiency
* Collaborating with engineers, data scientists, and domain experts to bring research into production

We're looking for a talented AI Security Researcher to join our team and play a critical role in our foundational, risk-driven approach to cloud security. This role requires deep technical research into complex cloud- and AI-native environments to identify the most significant, unaddressed risks.
WHAT YOULL DO
Conduct deep technical research to discover and report novel risks and attack vectors specific to modern cloud- and AI-native architectures and systems.
Discover and articulate the highest unaddressed risk areas, working with Product and Engineering teams to translate research into product capabilities.
Define necessary foundational product capabilities by delivering both compelling proofs of risk (demonstrating impact) and technical POCs (showing how to solve it).
Work closely with Product and Engineering teams to ensure comprehensive risk coverage and support the investigation of new and complex product scope.

Were seeking a Head of AI to lead research and engineering for multimodal foundation models and reasoning‑centric systems-from prototyping to reliable, secure, production deployment. The Head of AI will manage deep learning researchers, computational biologists, immunologists and engineers and partner closely with product leaders and company leadership to convert cutting‑edge methods into high‑impact, mission‑critical capabilities.

Location: Ramat Gan, Israel (hybrid model)

What will you do?

Team Leadership & Growth

Lead, mentor, and develop deep learning researchers, computational biologists, and machine learning engineers; set a high bar for scientific rigor, code quality, and delivery.
Establish clear ownership, role definitions, and growth paths; nurture a collaborative, low‑ego culture.
Technical Strategy & Delivery

Own the AI roadmap and architecture; guide model design, evaluation, and productionization (training, serving, observability, safety).
Build scalable pipelines for transformers, multimodal fusion, and reasoning/agent frameworks; champion reproducibility, CI/CD for models, and cost‑efficient GPU/TPU utilization.
Research Integration & Innovation

Stay current with the literature; run journal clubs and technical deep dives.
Evaluate, pilot, and integrate state‑of‑the‑art methods (advanced transformers, retrieval and tool‑use agents, causal/biological reasoning) into robust systems.
Cross‑Functional Collaboration

Translate biological and clinical questions into tractable AI projects with measurable impact.
Communicate complex trade‑offs to peers and executives; align plans, risks, and milestones across functions.
Mission & Impact

Prioritize initiatives that advance patient outcomes and create cumulative platform value.
Balance speed with scientific integrity, security, and compliance.

Required Applied AI Researcher- Sovereign AI Research
The Dream Job
Nations are waking up to a hard truth: critical intelligence infrastructure built on hyperscaler black boxes isn't a solution it's a dependency. The Sovereign AI Research Group exists to answer that differently.
We're not fine-tuning what already exists. We're rethinking the models architecture from the ground up modular, composable, and built with compute governance as a first-class design constraint, not an afterthought.
We operate under real-world constraints. The interesting problems live at the intersections of disciplines. That's where we operate.
This is a hands-on research role embedded within a team of senior researchers. Day to day includes training models, running benchmarks, synthesize data. The researchers you'll work alongside will challenge you technically and invest in your growth.
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.

Required Research Scientist - Sovereign AI Research
The Dream Job
Nations are waking up to a hard truth: critical intelligence infrastructure built on hyperscaler black boxes isn't a solution it's a dependency. The Sovereign AI Research Group exists to answer that differently.
We're not fine-tuning what already exists. We're rethinking the models architecture from the ground up modular, composable, and built with compute governance as a first-class design constraint, not an afterthought.
We operate under real-world constraints. The interesting problems live at the intersections of disciplines. That's where we operate.
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.

We are seeking a Senior Applied Researcher/ Data Scientist to join our core product organization, where you will build and deploy next-generation AI models powering real-world cybersecurity use cases across our diverse product portfolio (Posture, Detection, and CTI). The role centers on building and scaling intelligent systems powered by LLMs, advanced ML techniques, and agent-based architectures.
You will join a team within our Cyber R&D organization, leading the companys core product portfolio-while driving AI innovation and setting best practices across the domain. The group focuses on developing next-generation technologies, including training and optimizing LLMs and building advanced multi-agent workflows, while mentoring data scientists across R&D.
As a key technical leader, you will guide the evolution of our AI capabilities-converting cutting-edge ideas into impactful, production-ready systems and shaping our long-term direction.
We are looking for visionary applied researchers eager to take on ambitious challenges and push the boundaries of AI in cybersecurity.
Responsibilities
Design and build advanced AI models and systems that power our core products and address real-world cybersecurity challenges.
Own initiatives end-to-end from identifying customer-driven problems and shaping solutions to experimentation, prototyping, and production deployment.
Drive forward new ideas and research directions, turning emerging concepts into practical, scalable solutions.
Influence the organizations roadmap and contribute to long-term technical strategy across our AI and product landscape.

As our AI Applied Researcher, 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 hiring an AI Researcher to join the research team building the next generation of AI-native security systems. You will work alongside security and threat researchers to build large-scale AI agents that reason over software, code, endpoint activity, and security signals to detect malicious behavior, uncover vulnerabilities, assess risk, and make autonomous security decisions in real-world production environments. We are entering the Mythos era - where attackers operate at machine speed using autonomous systems and AI-generated software, and defenders must evolve the same way. We use state-of-the-art frontier models, including access to Mythos, to build reliable AI-native security systems at global scale. You will help design the evaluations, harnesses, and reliability infrastructure that make autonomous agents dependable under real customer load, while collaborating with leading AI organizations including Anthropic on initiatives such as Glasswing. This is an opportunity to work at the frontier of AI, autonomous systems, and cybersecurity while helping define how the next generation of security systems will operate.
Key Responsibilities
Build AI agents and autonomous security systems that reason over software, code, endpoint activity, MCPs, and security signals to detect malicious behavior, uncover vulnerabilities, and assess risk at production scale.
Develop systems, tooling, and infrastructure that enable agents to autonomously investigate threats, hunt for malware in massive datasets, and operate reliably in complex security environments.
Design and run experiments to evaluate frontier-model and agent capabilities in realistic adversarial scenarios, including benchmark creation, large-scale datasets, automated evaluations, and human-in-the-loop review systems.
Build the evaluation harnesses, observability systems, and reliability infrastructure required to make autonomous agents accurate, scalable, and dependable under real customer load.
Engineer for scale and performance across large distributed AI systems, including inference optimization, orchestration, batching, caching, cost controls, and graceful degradation under high demand.
Continuously evaluate emerging models, agent architectures, prompting techniques, and research directions to ensure our systems remain at the frontier of AI-native cybersecurity.
Rapidlyזprototype and test new approaches across reasoning, autonomy, evaluations, and security workflows as the AI landscape evolves.
Partner closely with threat and security researchers to extract domain expertise, translate analyst reasoning into AI workflows, and enable new forms of automation and autonomous investigation.
Collaborate with leading AI and security researchers to shape the future of AI-native cybersecurity as the industry transitions into the Mythos era.
Senior candidates will help define research direction, shape technical strategy, identify high-leverage problems, and influence how autonomous AI systems are deployed across the organization.

As a Senior Data Science Researcher in our Workforce Management group, you will be part of a cross-sectional team working at the intersection of machine learning, GenAI, agentic AI, ML-Ops and Operations Research. You will develop the next generation of our companys Workforce Management solution for our customers contact centers - forecasting work volume, scheduling agent shifts, managing time-off requests, and owning the full lifecycle of the work schedule, in a high-stakes production environment serving many customers. Our solution has been recognized as the market leader for over 10 years.
How will you make an impact?
Developing end-to-end ML systems, tailored to specific project needs, to train and serve production-level models in a high-stakes environment.
Deploying GenAI and agentic AI systems to customers in production, improving on existing solutions or creating new services from scratch.
Designing and building agentic AI flows - tool use, multi-step reasoning, and orchestration.
Forecasting work volume and optimizing shift schedules using time-series methods and mathematical optimization.
Integrating new features and solutions into large existing software systems, stringing multiple disciplines together.
Running unstructured evaluation processes - finding noteworthy phenomena, experimenting, and feeding conclusions back to the team.
Owning projects end-to-end, leading team members and colleagues to efficient planning and delivery.

As a Senior AI&ML Researcher in our Workforce Management group, you will be part of a cross-sectional team working at the intersection of machine learning, GenAI, agentic AI, ML-Ops and Operations Research. You will develop the next generation of our companys Workforce Management solution for our customers contact centers - forecasting work volume, scheduling agent shifts, managing time-off requests, and owning the full lifecycle of the work schedule, in a high-stakes production environment serving many customers. Our solution has been recognized as the market leader for over 10 years.
How will you make an impact?
Developing end-to-end ML systems, tailored to specific project needs, to train and serve production-level models in a high-stakes environment.
Deploying GenAI and agentic AI systems to customers in production, improving on existing solutions or creating new services from scratch.
Designing and building agentic AI flows - tool use, multi-step reasoning, and orchestration.
Forecasting work volume and optimizing shift schedules using time-series methods and mathematical optimization.
Integrating new features and solutions into large existing software systems, stringing multiple disciplines together.
Running unstructured evaluation processes - finding noteworthy phenomena, experimenting, and feeding conclusions back to the team.
Owning projects end-to-end, leading team members and colleagues to efficient planning and delivery.