דרושים » תוכנה » ML Platform Engineering Team Lead - Sovereign AI Engineering

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.

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.

we are looking for a Data science Team Lead.
As the Data Science Team Lead, you will lead a talented team of data scientists and ML engineers building the infrastructure, systems, and workflows for designing, training, evaluating, and deploying machine learning models that protect millions of users worldwide from fraud and account compromise.
This role combines hands-on technical leadership with people management and strategic ownership. You will drive innovation across real-time model serving, customer-specific model tuning, offline AI evaluations, and scalable ML systems in a production-grade SaaS environment.
If you are passionate about applied machine learning, fraud detection, and building intelligent systems at scale - we want you on our team.
What youl do:
Lead and mentor a team of Data Scientists and ML Engineers focused on fraud detection and response capabilities.
Build ML infrastructure focused on design, train, evaluate, and optimize machine learning models for real-time fraud prevention and risk assessment.
Own the lifecycle of ML models in production, including experimentation, deployment, monitoring, retraining, and performance optimization.
Drive customer-specific model training and tuning strategies to improve accuracy and adaptability across different customer environments.
Build and improve offline AI evaluation frameworks to measure model quality, drift, effectiveness, and business impact.
Collaborate closely with Engineering, Product, Security, and Data teams to deliver scalable and reliable AI-powered capabilities.
Define best practices for model serving, feature engineering, experimentation, observability, and operational excellence.
Balance model performance, latency, scalability, explainability, and operational constraints in high-scale production environments.
Promote a culture of technical excellence, continuous improvement, ownership, and innovation.

The MLIL DataPlane team is looking for a Senior Software Development Engineer to own the design and implementation of our inference data plane. We build the software that makes large models run efficiently on custom hardware - spanning model execution, memory management, data movement, and serving integration.
Our work covers the full inference path: integrating serving engines with custom hardware, developing high-performance compute kernels, enabling efficient data movement, and driving models from early validation through production. We operate at frontier scale with large distributed models.
This is a ground-up effort with rapidly evolving hardware and software. We need a senior IC who can write and optimize low-level code for custom hardware, validate model architectures end-to-end, build test and profiling infrastructure, and drive performance across the stack.

Key job responsibilities
- Develop and optimize compute kernels for a custom ML accelerator architecture, targeting production-level performance for large language model inference.
- Implement and validate LLM architectures (decoder-only, mixture-of-experts) end-to-end - from PyTorch model definition through distributed execution on custom hardware.
- Integrate custom accelerator backends into open-source ML serving frameworks (vLLM, PyTorch), including scheduler extensions, memory management, and model parallelism.
- Build and maintain test infrastructure for model correctness validation across CPU, GPU, simulator, and hardware targets.
- Profile and optimize inference workloads - identify bottlenecks, instrument critical paths, and drive latency and throughput improvements from simulation through hardware bringup.
- Own features end-to-end: from design through implementation, testing, and integration into the broader software stack.
- Contribute to CI/CD pipelines that gate model and kernel changes on correctness and performance regressions.
- Mentor engineers, drive design reviews, and raise the engineering bar across the team.

We're looking for a Platform Team Leader to own the foundational systems that power R&D organization. The Platform team is a horizontal, internal-facing team - its customers are the engineers, data scientists, and analysts on our application teams, and its mission is to make them faster, safer, and more productive.
You will lead a team responsible for our production-critical data ingestion (scraping system, data normalization platform), our developer experience surface (Python mono repo, packages and services framework, CI/CD, shared images), our Airflow base layer, and our backoffice. These systems sit on the critical path for production - every other team depends on them, and uptime, incident response, and reliability are core to the role.
This role is for a hands-on technical leader who enjoys both shipping code and growing engineers. You'll spend roughly 50% of your time hands-on writing code, reviewing designs, and the rest leading the team, partnering with peer team leads, and shaping the technical direction of our platform with the VP R&D and CTO.
Responsibilities
Lead and grow a team of strong backend engineers, owning hiring, mentorship, performance, and personal growth
Own the team's roadmap end-to-end: scoping, prioritization, delivery, and quality
Drive the architecture and evolution of the platform layers your team owns - both the data plumbing that feeds and the developer-experience surface other R&D teams build on
Contribute hands-on (~50%) to design, code, code reviews, and production debugging
Set and uphold engineering standards across the team - code quality, system design, testing, observability, and operational excellence
Partner closely with peer team leads and the VP R&D to align on shared infrastructure, ownership boundaries, and developer experience
Lead by example on production incidents and customer-impacting issues
Influence broader R&D direction as a member of the R&D leadership forum

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.

We're looking for a Senior AI Infrastructure Engineer to join a group that specializes in Security and Networking, and specifically ML/AI, MLOps, and agentic AI development. As a Senior AI Infrastructure Engineer, youll build and maintain the infrastructure, tools and processes necessary to support the AI lifecycle in a production environment. You will collaborate closely with data scientists, software engineers, and security architects to ensure smooth development, deployment, evaluation, and optimization of AI pipelines, models, and agents. This role requires a balance of high-level engineering rigor and a collaborative spirit; youll be a technical anchor and a supportive peer for teams across the organization.



What youll be doing:

Architecting, developing and optimizing scalable infrastructure for deploying security and networking AI models and agents in production.

Managing ML/agentic workflows to ensure performance, high availability, resource efficiency, and cost-effectiveness.

Designing and implementing pipelines and frameworks for AI training, inference, and experimentation.

Partnering with data scientists and security architects to operationalize AI agents, including packaging and integration with existing systems. This includes contributing to and reviewing code, design documents, and test plans.

Partnering with DevOps teams to integrate pipelines and workflows into CI/CD processes, ensuring reliable deployments and rollbacks.

Building proactive monitoring systems to identify issues in quality and infrastructure before they impact production.

Implementing access controls, authentication mechanisms, and encryption standards to keep our AI models and data secure.

Documenting guidelines and leading knowledge-sharing sessions to elevate the teams collective development expertise.

As a Senior AI Engineer in the global CTO group, you will play a central role in building the next generation of AI-powered security capabilities across our product portfolio. This role is focused on rapid prototyping, experimentation, and innovation, turning emerging ideas into working product features that can scale across multiple products and technology stacks.

You will design and build AI-driven systems end-to-end, from agent-based workflows and model integrations to backend services, data pipelines, and product-facing capabilities. You will work closely with product, engineering, and research teams across the company to explore new use cases, validate ideas quickly, and bring impactful AI features into production.

This role is ideal for an experienced AI engineer who enjoys moving fast, working across boundaries, and building real production systems, not just experiments. Your work will directly influence how AI is embedded across our platforms and how customers experience secure AI at enterprise scale.

we are looking for a Director, AI & Machine Learning.
As the Director of AI & ML you will serve as the strategic and technical anchor for our Israel based AI and Machine Learning functions reporting directly to the VP of Data & AI. You will own the delivery of the core capabilities that power our product bridging the gap between high level business strategy and production grade AI execution. This is a high impact leadership role designed for a senior technical partner who can drive cross organizational alignment across Product, Engineering and Platform teams.
We are looking for a leader who moves beyond a traditional management layer to represent the DS/AI function in leadership discussions and strategic planning while maintaining a close operating rhythm with global leadership. In this role you will ensure our AI initiatives are integrated into the heart of the product lifecycle while serving as a credible senior partner for the technical leads. You will be responsible for shaping the future of how our organization builds and scales intelligent systems that handle sensitive healthcare data with precision and scalability.
Scope & Team
This role owns two teams within the Data & AI group:
AI Team: Builds patient-facing AI capabilities, NLP models, and clinical decision-support features.
ML Team: Owns production ML pipelines, model monitoring, and machine learning infrastructure.
Beyond direct team management, this role carries significant cross-organizational strategic responsibility:
Product interface: Bridge the gap between PMs and DS/AI team leads. The TLs are invested at the project level - this Director operates at the strategic level, ensuring AI/ML priorities are shaped early in the product lifecycle, not bolted on after. Be a force of change in how Product and DS/AI collaborate.
Engineering interface: Work peer-to-peer with engineering directors on shared execution dependencies, technical architecture decisions, and resource planning. Own this relationship at the Director level rather than leaving it to individual TLs.
Platform interface: Partner with Platform on infrastructure, data pipelines, and tooling strategy that underpins ML/AI delivery. Ensure DS/AI needs are represented in platform roadmap decisions.