דרושים » תוכנה » Research Infra Engineer

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
Build and operate ML training infrastructure - distributed training pipelines, compute scheduling, and reproducible experiment workflows that data scientists rely on daily.
Own model serving and inference systems - packaging, deployment, autoscaling, A/B testing, canary rollouts, and latency/cost optimization for production models.
Run feature stores, model registries, and dataset versioning - enabling self-serve feature engineering, model lineage, and reproducible experiments across teams.
Build experiment tracking and evaluation infrastructure - automated evals, comparison dashboards, drift detection, and monitoring that give teams visibility into model behavior and performance.
Build and maintain production pipelines for training, fine-tuning workflows, and serving domain models - owning reliability, reproducibility, and scale.
Build and maintain the monitoring and observability layer - model performance tracking, data and prediction drift detection, data quality validation, and alerting.
Improve performance and cost across the ML stack - training throughput, inference latency, batch vs. real-time tradeoffs, and compute cost management.
Ship shared tooling - libraries, templates, CI/CD for models, IaC, and runbooks - while collaborating across Data Platform, AI, Data Science, Engineering, and DevOps. Own architecture, documentation, and operations end-to-end.

We're looking for an AI Engineer who is equal parts builder, enabler, and visionary.
This is a rare opportunity to join a small, elite team at the ground floor and have outsized impact on how AI is designed, built, and shipped across a globally recognized cybersecurity platform.
If you thrive at the intersection of cutting-edge AI research and real-world production systems and you want your fingerprints on something that matters - read on.
Why Join Us?
Greenfield opportunity - you're not joining a mature team with fixed patterns, you're helping define them.
Real impact at scale - your work will influence products used by thousands of organizations worldwide.
A team of great people - small, senior, and genuinely collaborative.
Freedom to innovate - we encourage bold ideas, fast experiments, and honest feedback.
our company's AI moment - AI is a company-wide strategic priority, and this group is at the center of it.
*we are an equal opportunity employer committed to diversity and inclusion.
Key Responsibilities
What You'll Do:
Build AI infrastructure - Design and develop the foundational tools, frameworks, and pipelines that power the group's AI capabilities, with a focus on LLMs and Generative AI.
Enable AI across the team - Act as the group's AI enablement engine: establish best practices, create internal tooling, and uplift teammates to work effectively with AI systems.
Own AI agents & agentic workflows - Design, implement, and iterate on autonomous agents and multi-step AI pipelines integrated with a variety of tools and environments.
Bring AI to production - Take models and capabilities from prototype to production-grade systems - reliable, scalable, and observable.
Shape the big picture - Contribute to the group's AI strategy, not just its execution. We want someone who asks "why" before diving into "how."
Stay ahead of the curve - Continuously research and evaluate emerging AI techniques, models, and tools - and bring what's relevant back to the team.
Collaborate and communicate - Write clearly. Think clearly. Work closely with researchers, engineers, and product stakeholders to align on goals and drive outcomes.

Join our companys AI research group, a cross-functional team of ML engineers, researchers and security experts building the next generation of AI-powered security capabilities. Our mission is to leverage large language models to understand code, configuration, and human language at scale, and to turn this understanding into security AI capabilities which will drive our company AI future security solutions.
We foster a hands-on, research-driven culture where youll work with large-scale data, modern ML infrastructure, and a global product footprint that impacts over 100,000 organizations worldwide.
Key Responsibilities
As a Senior ML Research Engineer, you will be responsible for the end-to-end lifecycle of large language models: from data definition and curation, through training and evaluation, to providing robust models that can be consumed by product and platform teams.
Own training and fine-tuning of LLMs / seq2seq models: Design and execute training pipelines for transformer-based models (encoder-decoder, decoder-only, retrievalaugmented, etc.), and fine-tune open-source LLMs on our company-specific data (security content, logs, incidents, customer interactions).
Apply advanced LLM training techniques such as instruction tuning, preference / contrastive learning, LoRA / PEFT, continual pre-training, and domain adaptation where appropriate.
Work deeply with data: define data strategies with product, research and domain experts; build and maintain data pipelines for collecting, cleaning, de-duplicating and labeling large-scale text, code and semi-structured data; and design synthetic data generation and augmentation pipelines.
Build robust evaluation and experimentation frameworks: define offline metrics for LLM quality (task-specific accuracy, calibration, hallucination rate, safety, latency and cost); implement automated evaluation suites (benchmarks, regression tests, redteaming scenarios); and track model performance over time.
Scale training and inference: use distributed training frameworks (e.g. DeepSpeed, FSDP, tensor/pipeline parallelism) to efficiently train models on multi-GPU / multi-node clusters, and optimize inference performance and cost with techniques such as quantization, distillation and caching.
Collaborate closely with security researchers and data engineers to turn domain knowledge and threat intelligence into high-value training and evaluation data, and to expose your models through well-defined interfaces to downstream product and platform teams.

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
Set technical direction for the ML platform - training pipelines, model serving, feature stores, experiment tracking, and compute orchestration - through RFCs, prototypes, design reviews, and build-vs-buy decisions
Lead and grow a team of ML Engineers - hire, mentor, pair on hard problems, and raise the bar through code and design reviews
Contribute to critical systems, debug production issues, and maintain deep context on the codebase to inform technical decisions
Own operational excellence for model serving - set and enforce SLAs, run capacity planning, and keep compute costs predictable
Establish ML engineering standards - reproducible experiments, automated evals, model packaging, CI/CD for models, and observability
Support the full lifecycle of our company's models - from training on domain-specific data to low-latency inference powering production systems
Work closely with Data Platform, AI, Data Science, and Product teams - translate business priorities into engineering work and manage cross-team dependencies
Measure and improve developer experience - deploy friction, onboarding time, CI turnaround - as seriously as model performance.

As a Senior DevOps Engineer supporting our Cortex Research Group, you will lead all DevOps and infrastructure initiatives that empower our researchers to move quickly, securely, and reliably. You will be responsible for designing, building, and maintaining the groups cloud environments, ensuring scalability, stability, and performance across a wide range of experimental and production workloads. Youll serve as the primary point of contact between the Research Group and other critical stakeholders-including Security, Networking, and Compliance teams-ensuring that research projects align with organizational standards while still enabling rapid innovation.
Key Responsibilities
Own and evolve the Research Groups cloud infrastructure and CI/CD pipelines to enable reproducible, automated, and scalable experimentation.
Define and implement standards for infrastructure-as-code, observability, monitoring, and resource optimization tailored to research use cases.
Proactively collaborate with security and compliance teams to enforce best practices for data governance, access controls, and regulatory requirements.
Partner with networking and platform engineers to integrate research workloads into the broader company ecosystem, ensuring seamless operation.
Serve as the primary technical liaison between the Research Group and stakeholders like Security, Networking, and Platform teams.
Mentor engineers and researchers on DevOps best practices, helping to instill a culture of operational excellence and applied learning.

We are looking for a Platform / DevOps Engineer to design, build, and operate the core infrastructure for a research compute datacenter. This platform supports researchers and physicists by providing scalable compute resources and exposing selected physics experiments to external users as Software-as-a-Service (SaaS).
What Youll Do
Design, maintain, and operate bare-metal Kubernetes clusters used for research and production workloads.
Build and manage declarative GitOps and workflows using tools such as Argo CD and Argo Workflows.
Develop and maintain Python-based infrastructure automation, backend services, APIs, and internal tooling for Kubernetes-based research platforms.
Administer and support core services such as Linux systems, Redis, and PostgreSQL.
Implement and evolve networking and security policies, including Cilium-based enforcement.
Collaborate with researchers to expose internal physics experiments as external SaaS services.
Contribute to internal platforms and, where possible, open-source projects.
Continuously improve reliability, observability, and developer/researcher experience.

We are looking for a hands-on Security Research Team Lead to own and scale our Security Research domain.
This is a foundational role in a small, highly technical team, focused on deep security research, algorithmic thinking, and leveraging AI to turn complex data into accurate, actionable security insights.
This role is not about people management by default - it is about technical leadership, ownership, and building scalable research capabilities that directly impact product and customer security.
Responsibilities:
Own and lead the Security Research domain through hands-on technical work.
Research security risks in SaaS and business applications, including permissions, identities, and access models.
Design and apply algorithmic approaches to analyze complex data structures and security risk surfaces.
Leverage AI-based techniques to work with large-scale data, improve accuracy, and automate research workflows.
Detect security flaws, misconfigurations, and systemic risks in SaaS environments.
Lead and support complex security investigations and customer-facing incident research.
Work closely with Product and Engineering to translate research findings into scalable product capabilities.
Technically mentor and support a small and growing research team.
Define research priorities and help shape how the team scales over time.

Were looking for driven and talented people like you to join our team and our mission to change the future of cloud security. Ready to dive in and swim with our pod?
As the Head of Research, you will lead Securitys threat-research, security-innovation, and vulnerability-discovery efforts. You will define the strategy for how we uncover threats, identify novel attack vectors, influence product direction, and contribute thought leadership to the cybersecurity community. You will manage and grow a team of world-class researchers, work closely with product, engineering and go-to-market teams, and ensure our research remains cutting-edge, rigorous and impactful. This role emphasizes strong people leadership and cross-functional execution, alongside technical depth and hands-on research judgment.
What youll do :
Develop, own and evolve the research strategy by defining high value focus areas (for example misconfigurations, identity threats, workload vulnerabilities, and emerging attack techniques), and ensure alignment with product roadmap and business objectives.
Lead, coach, and mentor a multidisciplinary research team (researchers, threat analysts, and engineers).
Build a healthy, high-performing org, including hiring, onboarding, and performance management.
Partner closely with product and engineering leadership to turn research insights into concrete roadmap items, detection logic, and customer value.
Drive discovery of new vulnerabilities, attack techniques, or adversary behaviors across cloud and modern infrastructure environments (for example containers, serverless, data stores, IAM).
Define metrics for research impact (for example vulnerabilities discovered, time to validate and operationalize new findings, research-driven product improvements, external reach).
Establish and maintain external partnerships (industry peers, academic groups, independent researchers) to expand capabilities and pipeline.
Publish and present research findings (blog posts, white papers, conference talks).
Lead vulnerability disclosure and responsible communications.
Ensure the research function has the right infrastructure and processes (tooling, sandboxes, repeatable experimentation, documentation standards).
Stay current with the threat landscape, emerging technologies, attacker tradecraft, and relevant compliance or regulatory shifts.

As a Senior Malware Researcher, you will analyze and reverse engineer malware and tooling used by advanced threat actors, particularly those targeting governments and critical infrastructure. You will apply deep expertise in binary and script analysis to uncover capabilities, configurations, and C2 infrastructure for threat actor attribution and mitigations.
Your work will power our companys malware catalog, strengthen our understanding of threat actors technical capabilities, and directly support the development of next-generation AI-driven "artificial cyber researchers".
Responsibilities
Perform in-depth static and dynamic analysis of malware, implants, loaders, and related tooling used by APTs and other sophisticated adversaries.
Reverse engineer binaries and scripts (e.g., PE, ELF, .NET, PowerShell, JavaScript/VBA) to determine capabilities, execution flow, persistence mechanisms, and evasion techniques.
Extract and document configuration data and C2 information, and map these to campaigns, infrastructure, and threat actors in collaboration with CTI researchers.
Develop and maintain detection and hunting artifacts such as YARA rules, VT LiveHunt queries, CAPA rules, and sandbox behavior signatures.
Work closely with CyberAI researchers on the development of next-generation artificial cyber researchers and AI-driven analysis capabilities.