We are looking for a Principal MLOps Engineer with a deep focus on ML Platforms and Infrastructure to join our Data & AI group at Cortex Research. Our team is responsible for designing, building, and scaling the foundational MLOps and LLMOps platforms that power both our Data Scientists and Security Researchers. You will architect the high-performance core infrastructure that enables these roles to build, train, and deploy advanced AI systems-ranging from optimized Small Language Models (SLMs) to complex agentic workflows and RAG systems. If you are passionate about building scalable compute platforms and automating the full ML lifecycle to solve complex data and security challenges, we want to hear from you.
Key Responsibilities
Scale Distributed Training: Design and optimize infrastructure for training and fine-tuning LLMs and SLMs, leveraging distributed GPU workloads, efficient clustering, and compute optimization.
Automate the ML Lifecycle: Architect robust, automated pipelines for continuous training (CT) and deployment (CD) of models, ensuring a seamless flow from raw data collection to production environments.
Build Model Infrastructure: Own the serving architecture for LLMs/SLMs, balancing latency, throughput, and GPU utilization under production traffic.
Implement Advanced Monitoring: Establish comprehensive observability systems to monitor live model performance, data drift, and computational metrics, feeding insights back into the automated training loops for continuous improvement.
Collaborative Architecture: Partner closely with data scientists and security researchers to productize complex model architectures and streamline their workflows, while collaborating with our DevOps team to integrate with core cloud infrastructure.

We are looking for a proactive and skilled Software Infrastructure Developer with a strong Agentic AI focus to join our team. In this role, you will design and build the infrastructure, tooling, and AI-powered automation that keep our engineering systems reliable, scalable, and intelligent.

You will be responsible for embedding GenAI and agentic capabilities into our internal developer platforms and CI/CD workflows - creating frameworks, libraries, and multi-agent systems that accelerate automation, improve system stability, and unlock new levels of engineering productivity. This is a hands-on role for someone who thrives at the intersection of infrastructure engineering and applied AI.

The ideal candidate brings deep technical expertise in Python (FastAPI) and/or TypeScript, Kubernetes, CI/CD (Jenkins, GitHub Actions), containerization, and cloud infrastructure - combined with hands-on experience building LLM-powered applications, agents, MCP servers, and RAG pipelines.

What you'll be doing:
AI Platform Development: Design and build internal AI platforms, reusable SDKs, libraries, tools, and MCP servers for agentic applications.
Agentic Workflow Development: Develop single- and multi-agent workflows covering planning, tool selection, delegation, state and memory management, execution, retries, fallbacks, error recovery, and human-in-the-loop approval.
AI-Powered Infrastructure & CI/CD: Integrate AI capabilities into Kubernetes infrastructure and Jenkins/GitHub Actions pipelines to improve development, testing, deployment, and operational workflows.
AI Observability & Evaluation: Implement agent tracing, prompt and tool-call logging, latency and error monitoring, token and cost tracking, regression testing, and failure analysis.
AI Security & Governance: Apply guardrails, access controls, auditability, and governance practices to ensure agents operate safely and reliably.
End-to-End Platform Ownership: Own AI platform capabilities from architecture through production and build reusable solutions that accelerate adoption across engineering teams.
Cross-Functional Collaboration: Collaborate with engineering and business teams to identify high-impact AI opportunities and translate them into scalable technical solutions.
AI Research & Innovation: Evaluate emerging AI frameworks, models, and cloud technologies and promote their adoption where they provide measurable value.

We are looking for a Senior AI Engineer to design and build the intelligence layer.
You will own the agentic workflows, LLM integrations, and reasning pipelines that allow our AI agents to autonomously analyze markets, make decisions, and drive eCom growth at scale.
What you'll do:
Design & Build Agentic Workflows: Architect multi-agent pipelines - including planning, memory, tool use, and decision loops - that power autonomous media buying and growth operations.
Own LLM Integration: Select, prompt-engineer, fine-tune, and evaluate LLMs to produce reliable, high-quality outputs across diverse business tasks.
Build RAG Systems: Develop retrieval-augmented generation pipelines with vector search and context management to ground agent reasoning in real business data.
Drive Evaluation & Reliability: Define evals, build testing frameworks, and continuously improve agent output quality, consistency, and safety in production.
Collaborate Across the Stack: Work closely with backend engineers to integrate AI capabilities into core product APIs, ensuring low-latency, production-grade deployment.

We're seeking a Staff engineer - AI Builder to lead the design and implementation of AI-native technical frameworks that redefine how large-scale systems are built, extended, and evolved.

This role is deeply technical and architecture-driven, focused on designing modular, extensible, and AI-first system foundations that enable scalable development with AI as a core engineering collaborator.

You will work across the full stack, building infrastructure where AI actively generates, extends, and maintains software components as part of the native development flow.

This is not about incremental AI integration - this is about architecting the frameworks that let AI scale software engineering 10x faster and smarter.

Responsibilities
Architect and lead the development of AI-native system frameworks that emphasize modularity, extensibility, and adaptive scalability.
Build platform primitives that enable dynamic AI-driven module and extension generation.
Design and implement developer workflows optimized for AI-assisted software development, embedding AI collaboration as a first-class design principle.
Define and codify AI-native engineering practices, patterns, and guidelines to elevate our software development lifecycle.
Establish and champion best practices for AI-assisted engineering across system design, code quality, testing, observability, and operational scalability.
Drive hands-on prototyping and iterative delivery across frontend, backend, and orchestration layers.
Provide technical leadership and mentorship, fostering an AI-first engineering culture across teams.
Partner closely with Product, AI, and Engineering teams to align system evolution with AI-native goals.

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
Your Impact & 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.

As a Technical Account Manager (TAM) in AWS Enterprise Support, you will serve as the primary technical advisor and voice of the customer for a portfolio of fast-growing Israeli startups. You will play a crucial role in fostering their innovative and transformative work across AI/ML, compute (including accelerated and GPU workloads), containers, networking, storage, databases, serverless, security, and more. It's an opportunity to work closely with technical founders and engineering leaders, helping them make the right architectural decisions at each stage of their growth journey.

Key job responsibilities
- Act as the primary technical advisor for your startup customers, building trusted-advisor relationships with CTOs, VP Engineering, and founding teams
- Engage across the full customer organization, from individual engineers through to C-suite, adapting your communication to each audience
- Help accelerate customer growth by crafting strategies that balance startup velocity with Well-Architected best practices across AI/ML, compute, containers, and data infrastructure
- Conduct architecture reviews, operational assessments, and proactive risk identification. Analyze service events and operational patterns to drive continuous improvement in resilience, performance, and cost efficiency
- Drive technical discussions with founding teams on architecture trade-offs, incident response, and risk management as their systems scale
- Uplift customer capabilities by running workshops, enablement sessions, and brown bag sessions tailored to their technical needs
- Champion and advocate for your customers by connecting their feedback to AWS engineering teams, influencing platform evolution, and ensuring rapid resolution of concerns
- Collaborate with Solutions Architects, Business Developers, Professional Services, and Account Managers to deliver coordinated customer value
- Develop and share technical content such as blog posts, reference architectures, and reusable solutions that help startups solve common challenges and reduce time-to-market.

This is a Principal AI Software Engineer role at the heart of our companys AI Engineering group-where algorithms, infrastructure, and products intersect. Youll lead the evolution and scaling of our companys AI architecture, ensuring reliability, efficiency, and impact across global clinical workflows. The role combines technical depth with cross-functional influence, strengthening how our AI systems are built, integrated, and delivered to drive ongoing impact for patients and care teams.
Responsibilities
Lead cross-domain engineering initiatives connecting AI algorithms (CV, NLP, and deep-learning systems), software, and data systems-from design to delivery.
Stay hands-on, contributing to AI-serving (training and inference), integration, and orchestration components while setting high technical standards.
Partner with Algo teams to connect algorithmic innovation with strong system design, ensuring smooth delivery across Product and Engineering.
Guide architectural evolution, ensuring efficiency, consistency, and maintainability across AI workflows.
Communicate direction and priorities clearly, aligning teams around technical goals.
Mentor engineers and promote technical excellence, sound design, and shared best practices.
Foster collaboration and shared ownership, helping maintain a culture that values clarity, curiosity, and technical excellence.
Uphold quality and reliability-including clean code, testing, and CI/CD discipline-while evaluating and adopting new tools that enhance performance and long-term scalability.

What You'll Actually Be Doing
Build and ship high-quality, production-grade software using modern engineering practices, with AI as a core part of your development workflow by pushing the boundaries of AI development tools to deliver secure, optimized, and high-quality code.
Design and orchestrate complex systems where AI agents integrate seamlessly into human workflows, driving efficiency and innovation at scale.
Contribute to building and maintaining the shared system context, an explicit repository of system designs, constraints, and standards that enables AI to operate accurately and reliably.
Critically evaluate code (human or AI-generated) for correctness, quality, security, and performance.
Build new and exciting components in an ever-growing and evolving market technology to provide scale and efficiency.
Develop high-quality, production-ready code that can be used by millions of users of our cloud platform.
Make design decisions on the basis of performance, scalability, and future expansion.
Work in a Hybrid Engineering model and contribute to all phases of SDLC including design, implementation, code reviews, automation, and testing of the features.
Build efficient components/algorithms on a microservice multi-tenant SaaS cloud environment.
Code review, mentoring junior engineers, and providing technical guidance to the team (depending on the seniority level).

Were looking for a Hands-on AI & Data Engineering Manager to lead a team building intelligent, large-scale consumer experiences powered by AI. In this role, you will lead engineers developing AI-driven products, work closely with data scientists, product managers, and designers, and ensure that AI capabilities-from LLMs and agents to product data insights-are translated into reliable, scalable production systems. You will be responsible not only for delivering AI features, but also for analyzing product data and user behavior to continuously improve AI performance and product outcomes. This is a technical leadership role combining engineering management, architecture ownership, AI product development, and data-driven decision making.

Key responsibilities

Lead a team of data scientists, data engineers and product analysts.

Own the delivery of AI-powered product capabilities, from research and experimentation to production and operation.

Drive excellence, code quality, and best development practices.

Provide technical direction and hands-on guidance for complex AI systems.

Drive the integration of LLMs, AI agents, and intelligent workflows into core consumer experiences.

Ensure AI solutions are safe, scalable, observable, and continuously improving.

Lead initiatives around product data analysis and experimentation.

Analyze user interactions with product features to improve accuracy, UX, and business impact.

Partner with product teams to define metrics, dashboards, and experiments that guide product improvements.

Design system architectures for AI-enabled applications at scale.

Evaluate and select technologies for AI platforms and data pipelines.

Guide the development of prompt engineering frameworks and centralized prompt management.

Ensure robust monitoring, evaluation, and feedback loops for AI outputs.

Translate product and business goals into technical roadmaps and execution plans.

Drive alignment between AI capabilities and measurable product outcomes.

we are looking for a AI Engineer.
Our R&D teams build and own the features that power platform - from deep code analysis to risk detection to developer-facing workflows. AI isn't a separate initiative here; it's how we build. Every engineering team is actively embedding LLMs, AI agents, and generative AI into the products they own, moving from experimentation to production and keeping things running reliably once they're there.
What You Will Do
Build and ship end-to-end AI-driven features and agentic workflows- from experimentation through validation to production deployment (Python and C#).
Design scalable backend services that support production-grade agentic AI capabilities.
Develop and improve solutions powered by LLMs, AI agents, multi-agent systems, and generative AI frameworks.
Evaluate agentic architectures and model performance, owning monitoring, reliability, and continuous improvement in production.
Collaborate cross-functionally with software engineers, data scientists, and analysts to drive AI innovation across the platform.