דרושים » AI » Senior Applied Researcher- AI Research

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.

We're looking for a Principal Cloud Security Researcher to serve as a senior technical leader within our Research team. This is a high-impact individual contributor role -- you won't manage people, but you'll shape the direction of our entire research function, mentor researchers, and act as a force multiplier across the organization.
You'll be the person who takes a vague threat signal and turns it into a detection strategy, a published finding, or a product capability. You'll operate as a trusted deputy to the research team lead, owning the most complex and ambiguous research challenges while raising the technical bar for the team.
What You'll Do
Drive Groundbreaking Research
Own and drive our most critical research initiatives end-to-end - from initial threat hypothesis through detection logic, product integration, and external publication.
Set the technical direction for cloud threat research across AWS, Azure, and GCP, identifying emerging attack surfaces and novel techniques before they become mainstream threats.
Investigate real-world cloud and SaaS security incidents, dissecting attacker tradecraft and extracting insights that evolve our detection capabilities.
Pioneer new forensic investigation techniques and detection methodologies for cloud-native and SaaS environments - pushing the state of the art, not just following it.
Be a Voice in the Community

Represent our company as a thought leader through high-quality research publications, conference presentations (BlackHat, DEF CON, RSA, fwd:cloudsec, and similar venues), and open-source contributions.
Build and maintain our reputation as a research-driven company that advances the field - not just a vendor with a blog.
Engage with the broader security research community, fostering relationships and collaborative knowledge-sharing.
Shape the Product

Bridge research and product - translate threat findings into actionable product requirements, working closely with engineering and product teams to ensure our CDR platform stays ahead of evolving threats.
Design and develop advanced detection algorithms that directly feed into our platform, closing the gap between research insight and customer protection.
Elevate the Team
Act as the team's go-to technical authority. When researchers hit a wall on complex cloud attack chains, IAM edge cases, or detection gaps - you're who they turn to.
Mentor and grow other researchers through research reviews, pair investigations, code reviews, and by setting quality standards and methodology best practices.
Influence technical decisions org-wide - contributing to architecture, tooling, and strategic research priorities.
Step in as the research team lead's deputy when needed - driving prioritization, representing research cross-functionally, and ensuring continuity.

We are seeking a highly accomplished AI Researcher to be part of the development of next-generation Foundation Models and Generative AI systems. In this role, you will bridge the gap between cutting-edge academic research and production-grade AI infrastructure. You will build, design, and fine-tune large-scale architectures (including LLMs, Multimodal, and Tabular Foundation Models) while applying them to complex, real-world domains like agentic behavior, coding and decision making.
In your day-to-day, you will:
Conduct pioneering research in deep learning and generative AI, specializing in Foundation Models, NLP, alignment, and multimodal learning
Deal with the impact of the models on production traffic, cost quality and latency
Train and scale state-of-the-art architectures using techniques like Post-Training SFT, Knowledge Distillation, LoRA, and sparse/hybrid model architectures
Own the AI lifecycle-from algorithmic prototyping and synthetic data generation to deploying production-grade AI features in fast-paced or big-data environments
Drive research initiatives, author publications/patents, and collaborate with other research teams to integrate intelligent capabilities into core products.

At UVeye, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we pioneered the world's first fully automated suite of AI-powered vehicle inspection systems, combining computer vision, machine learning, and generative AI. With over $380M in funding and strategic partnerships with Toyota, Amazon, General Motors, JLR, Volvo, and Hertz, our technology is deployed across manufacturing plants, dealerships, wholesale auctions, fleets, and seaports worldwide. Named one of Fast Company's Most Innovative Companies of 2026, our 300+ global employees are solutions-oriented, accountable, and driven by one shared goal: making roads safer for everyone. We are looking for an experienced Agentic AI Researcher to join the CTO’s office and help build the agentic backbone that automates work across UVeye, including R&D, product, sales, CS, marketing, finance, ops, legal, PM, and HR. In this role, you will partner with a small, senior research team to take agentic systems from idea to production, with direct visibility on company-wide impact. A day in the life and how you’ll make an impact
* Design and develop production-grade agentic systems that automate end-to-end workflows across UVeye’s business functions.
* Research and apply state-of-the-art techniques in tool use, planning, long-horizon reasoning, memory, and multi-agent coordination.
* Build robust evaluations, guardrails, and observability so agents are safe, measurable, and continuously improving.
* Partner with function owners to identify high-leverage automation targets, ship working agents, and quantify the workload reduced.

We are seeking a remarkable master student researcher to join our CTO office research team. In this role, you'll take a major part in groundbreaking research projects focused on detecting and mitigating cyber-attacks on AI applications as well as traditional applications using advanced analytics and emerging AI technologies.

We're looking for a highly self-motivated individual who thrives on challenges, works both independently and in unique small teams, and can rapidly turn bold ideas to the next production-grade cyber security capabilities.

What will you do?
Take a major part in innovative research initiatives in AI, application and agentic security, addressing complex cybersecurity challenges.
Design and develop AI agents that extend our detection and mitigation arsenal.
Create novel cyber security solutions to counter emerging threats in LLMs, autonomous agents, and legacy systems alike.
Use modern AI models, means and development tools.
Drive projects end-to-end - from concept through production-ready implementation.
Collaborate with cross-functional teams to integrate new capabilities into our product portfolio.

Required AI Researcher - World Model
Description
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.

Our work in Networking and AI is transforming the world's largest industries and profoundly impacting society. Our Networking product security team is looking for an outstanding technical AI red teamer with hands-on safety and security experience to help us improve the safety posture of AI models, systems and infrastructure. In this role you will reduce risk, threats, and vulnerabilities in our networking AI products.

What you'll be doing:
Drive hands-on safety and security research on a range of AI and networking products.
Develop tools and processes to expose novel weaknesses in AI models and systems to preempt threats.
Participate in defining and ensuring AI development processes meet safety and security standards.
Partner with cross-functional teams to understand needs and implement solutions.
Be a technical focal point across multiple teams and provide hands-on AI safety, security and engineering expertise.

Required Developer Advocate
This role sits at the intersection of engineering, product, and the global developer community. Youll translate our technical work across all domains, repositories, security, release management, pipelines, distribution, metadata, AI/ML, and ecosystem integrations, into content, demos, talks, and conversations that developers trust. Youll also bring community insights back into our R&D and business teams to influence product direction.
As a Developer Advocate you will:
Represent us across developer, DevOps, security, and platform-engineering communities through conferences, meetups, webinars, podcasts, and online conversations
Create high-quality technical content: deep-dive articles, demos, hands-on guides, screencasts, code samples, and social content
Build compelling demos and storytelling around our core domains: Artifactory, Xray, Curation, Distribution, Pipelines, Workers, Fly, AI/MCP, and ecosystem integrations like GitHub
Collaborate closely with R&D, Product, Security, Solutions Engineering, Architecture, and Customer Success to deeply understand new capabilities and provide practical feedback
Engage with open source and developer communities, finding new channels and conversations where we should have a voice
Partner with Marketing to strengthen messaging while preserving technical credibility
Monitor and report impact across community engagement, content performance, OSS participation, and broader DevRel KPIs.

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.