דרושים » תוכנה » AI Security - AI Platform Team Lead

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 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 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.

This is a leadership role within our companys core AI group, focused on building and evolving the AI-facing software systems that enable our algorithms to move from research into robust, production-ready components.
The AI Software team is responsible for developing the software layers, tooling, and core systems that support the development, execution, and operational readiness of AI models throughout their lifecycle. As a Team Lead, you will shape the teams technical direction, guide execution, and remain deeply hands-on, contributing to architecture, implementation, and key technical decisions.
You will lead a scrum team, work closely with AI researchers and algorithm engineers, and collaborate with product and engineering partners. The role combines strong software engineering with AI context, in a culture where software quality is a core enabler of AI excellence, and technical leadership is grounded in ownership, clarity, and impact.
Responsibilities
Lead the teams work on core software systems that sit at the intersection of algorithms and engineering, owning their design and implementation as part of the overall solution.
Guide and support team members growth, providing technical leadership, mentoring, and day-to-day coaching while fostering ownership and accountability.
Stay deeply hands-on in design and implementation, contributing code and solving complex software engineering problems where impact is highest.
Collaborate closely across teams, working with algorithm engineers, Product, and Engineering partners to define interfaces and align priorities.
Own project-level architecture and technical decisions, ensuring high-quality systems through sound design, code reviews, testing practices, and clear documentation.

Were looking for an experienced AI Team Lead to build and lead this team - someone who combines strong engineering leadership with hands-on experience building production AI systems and platforms.

What youll do
Build and lead AI dedicated team from the ground up.

Define, implement and maintain the company-wide AI platform, infrastructure, and developer tools.

Enable R&D teams to build, test, deploy, and monitor AI-powered capabilities independently.

Lead the design and delivery of shared AI experiences and services across the company.

Establish standards for quality, reliability, security, observability, and cost management.

Partner with Product, Engineering, DevOps, Security Research, and other teams to identify high-impact opportunities and turn them into scalable solutions.

Stay hands-on with architecture, implementation, production issues, and technical decisions.

Mentor engineers and raise the engineering bar for AI development across R&D.

Required Developer Experience (DevEx) Team Lead
As a Developer Experience (DevEx) Team Lead, you'll lead the team responsible for the platforms, tooling, and workflows that power our engineering organization. You'll help hundreds of engineers build, test, deploy, and operate software more efficiently by removing friction from the software development lifecycle. This is a leadership role where you'll shape technical direction, grow a high-performing team, and drive initiatives that improve engineering productivity across the company.
You'll Own:
Developer Experience Platform- Drive the evolution of our internal developer platform, enabling self-service capabilities across local development, CI/CD, testing, deployment, and observability.
Team Leadership & AI Adoption- Lead, mentor, and grow a team of platform and software engineers while driving the adoption of AI-powered development tools that accelerate engineering productivity at scale.
Engineering Productivity- Own the reliability and scalability of CI/CD systems, improve software delivery workflows, and partner with Infrastructure, Security, AI Enablement, and R&D leadership to continuously raise engineering effectiveness.
You'll Solve:
Remove bottlenecks across the software development lifecycle by building scalable platforms, automation, and self-service solutions that enable engineers to move faster with confidence.
Evaluate, operationalize, and scale emerging AI capabilities while ensuring they are adopted safely, effectively, and in ways that create measurable impact across R&D.
You'll Impact:
Increase engineering productivity and developer satisfaction by delivering reliable internal platforms and workflows that support hundreds of engineers across the organization.
Shape how software is built by influencing engineering standards, improving delivery speed and quality, and enabling teams to innovate more efficiently.
*We operate in a flexible hybrid work model.

We're looking for an Experienced Performance Researcher to join our founding team. Youll be responsible for building and optimizing scalable cloud infrastructure solutions tailored for AI workloads. This role offers a unique opportunity to directly shape our infrastructure strategy, improve system reliability and performance, and contribute to establishing us as a leader in adaptive AI compute management.
Join us to tackle the magic that make AI tick under the hood and build the backbone powering the AI revolution.
What Youll Do
- Design and build high-performance distributed inference pipelines for LLMs, focused on large-batch, non-real-time scenarios.
- Optimize GPU memory usage, kernel execution, and communication across nodes (NCCL, MPI, etc.).
- Own CUDA kernels, compiler-level tricks, and multi-GPU scheduling logic.
- Lead profiling and performance tuning for throughput, and cost- down to the kernel level.
- Collaborate with infra, product, and research teams to define SLAs, resource allocation logic, and runtime behaviors.
- Help build the core infrastructure that will run LLM workloads across hybrid GPU environments (cloud/on-prem/self-hosted).

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.

we are looking for a Lead Endpoint Researcher.
What You'll Do
Research AI coding agent runtimes, reverse-engineer how tools such as Claude Code, Cursor, Copilot CLI, Codex, and emerging agents execute commands, access files, spawn processes, and interact with the operating system.
Design agent sandboxing architectures that safely constrain filesystem access, networking, process execution, credentials, and other sensitive endpoint capabilities.
Explore and implement OS-native isolation mechanisms across macOS, Windows, and Linux, including containers, namespaces, sandbox profiles, virtualization, restricted processes, and permission models.
Build endpoint "harnesses" that allow TokenShift to intercept, control, observe, and modify agent execution flows.
Design infrastructure for deploying and running local LLMs across heterogeneous endpoint fleets, accounting for operating system, CPU architecture, GPU availability, memory constraints, and hardware acceleration.
Evaluate local inference runtimes and model formats such as llama.cpp, MLX, ONNX Runtime, Ollama, vLLM-compatible runtimes, and emerging alternatives.
Develop mechanisms for model discovery, installation, versioning, updates, health monitoring, and lifecycle management across large numbers of developer machines.
Research hardware-aware model selection and routing, deciding which models can run effectively on Apple Silicon, NVIDIA GPUs, Windows workstations, Linux machines, CPU-only environments, and other endpoint configurations.
Build benchmarks that measure model latency, throughput, memory consumption, startup time, resource contention, and developer experience on real endpoint hardware.
Investigate security boundaries between local models, cloud-hosted models, agents, MCP servers, shells, and enterprise resources.
Collaborate with the core CLI and platform teams to take endpoint research from prototype through production.
Track the rapidly evolving AI agent, sandboxing, and local inference ecosystems and proactively identify technologies should support.

As an AI Researcher, youll be part of the team building the AI systems that power our products. Youll work across research and engineering - from training and post-training state-of-the-art LLMs to applying them to challenging real-world problems such as agentic behavior, coding, and security-related decision-making.

This is a hands-on research role with a strong focus on taking ideas from experimentation to production. Youll have significant ownership and the opportunity to shape both the direction of our AI research and the systems we build around it.

WHAT YOU WILL DO
Train, adapt, and scale state-of-the-art LLMs using techniques such as post-training SFT, knowledge distillation, LoRA, and reinforcement learning.
Develop and evaluate new algorithms and approaches, bridging cutting-edge academic research with production-grade AI systems.
Optimize models for production, balancing quality, latency, throughput, and cost at scale.
Own the AI lifecycle-from algorithmic prototyping and synthetic data generation to evaluation, deployment, and iteration of production AI features.
Work closely with engineering and product teams to translate research advances into impactful AI capabilities.
Lead research initiatives that advance the capabilities of our AI systems.