We are looking for an MLOps Team Leadto drive the development of an internal machine learning platform for a group of ML teams. This is a hands-on leadership role: you will guide a small team of MLOps engineers that builds the automation and infrastructure powering our research (R&D) workflows and runs our pipelines to production. You will take ownership of end-to-end initiatives and drive the team toward critical infrastructure and model-lifecycle milestones, while staying close enough to the code to set technical direction and raise the bar by example.

You will be responsible for building and maintaining the models and data pipelines behind our data science workflows, ensuring the accuracy, consistency, and efficiency of the data used for training and inference, working across structured and unstructured data from many sources on a large-scale, distributed platform.

Responsibilities:

Lead, mentor, and grow a team of MLOps engineers, owning delivery and technical quality.
Take end-to-end ownership of infrastructure and pipeline initiatives across the LMM group, from design through production.
Stay hands-on: contribute to design and code, review work, and set engineering standards.
Drive the team through critical milestones in ML model-lifecycle and infrastructure ownership.
Partner with R&D and other stakeholders to translate research needs into robust, scalable systems.
Help evolve the platform, including our ongoing migration from Dask to Ray.

We are seeking an MLOps Engineer to help us grow our technical team's capabilities. The ideal candidate has relevant experience in data engineering, preferably within the AI field. Aviation industry experience would be a great addition.

You will be responsible for building and maintaining models and data pipelines that power our data science workflows. You'll play a crucial role in ensuring the accuracy, consistency, and efficiency of the data we use for model training and inference. This involves working with both structured and unstructured data from various sources, leveraging your expertise in data engineering and machine learning to create a robust and scalable system.

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 strong technical leader to lead one of our product-focused R&D teams.
This team owns meaningful product capabilities used across the platform and works closely with Product, Design, and other engineering teams. The role combines people management, technical leadership, and strong product thinking - from understanding customer needs and shaping solutions to delivering high-quality features in production.
You will lead a team of experienced engineers, develop their skills, set clear priorities, and create an environment of ownership, collaboration, and continuous improvement. At the same time, youll stay close to the technology, lead design decisions, and step into the code when needed.
What Will You Do?
Lead, mentor, and develop a team of engineers.
Own the teams execution, priorities, and long-term direction.
Work closely with Product and Design to turn customer needs into simple, scalable solutions.
Lead features and projects from early definition and design through production.
Drive technical design and architecture decisions while balancing product needs, quality, and delivery.
Stay close to the technology through design discussions, code reviews, debugging, and hands-on contribution when needed.
Build strong collaboration with other teams and lead cross-team initiatives.
Create clear processes and ownership as the team and its scope continue to grow.
Take part in hiring and help shape the teams professional standards and culture.
Use modern AI tools such as Cursor and Claude Code as part of the development workflow while maintaining high-quality engineering standards.

Were looking for driven and talented people like you to join our Engineering team and our mission to change the future of cloud security. Ready to dive in and swim with our pod?

As a Team Manager on the Orchestrator team, youll lead one of our most strategic engineering teams, responsible for the scan pipeline end to end, from trigger to published results. The orchestration layer this team owns sits on the critical path of the entire product, and every scan our runs goes through it.

The platform is large-scale, distributed and always on, running across all of our production sites worldwide, including regulated and customer-hosted environments. It is a demanding distributed-systems domain, where throughput, resilience and cloud efficiency are permanent engineering concerns rather than one-off projects.

Youll lead a team of talented senior engineers, providing both technical and people leadership while staying hands-on in design and implementation. Youll drive key architectural decisions, lead the platforms move to its next-generation architecture, and evolve it for higher scale, stronger resilience and deeper visibility into every job in flight.

Were looking for an experienced Team Leader who wants to make a foundational impact on the engine that powers our cloud security platform.

Our Stack: Python, AWS, Kafka, Redis, SQS, Postgres, Kubernetes. Our scanning surface extends across Azure, GCP, OCI and Alibaba Cloud and others.

What Youll Do:
Lead, mentor and develop a team of senior software engineers through one-on-ones, feedback and technical guidance.
Own and drive the delivery of high-impact projects end to end, from design through deployment and production monitoring.
Design and review architecture for large-scale distributed systems, with a focus on scalability, resilience and efficiency.
Drive the platforms scalability and cost efficiency as scan volumes grow.
Collaborate cross-functionally with Product, SRE and other R&D teams to define priorities, interfaces and success metrics.
Ensure the reliability, scalability and observability of core services, introducing SLOs and best practices for operations.
Uphold the teams engineering standards for code quality, safe rollout of changes and long-term maintainability.
Write and review clean, maintainable and tested code.

We are looking for a Senior Data Engineer to build and scale the data foundation behind platform and products. You will own complex data end-to-end - from ingestion and transformation through modeling, quality, observability, and production delivery.
This is a hands-on senior IC role for a strong builder who can solve difficult data problems independently, set a high technical bar, and collaborate closely with engineering, DS, and product. You will turn large, fragmented datasets into reliable, reusable capabilities that power every product.
Responsibilities
Build and own scalable data pipelines- Design, implement, and operate robust pipelines for high-volume structured and unstructured data, with validation, monitoring, lineage, and recovery built in.
Scale the platform for growth- A key near-term initiative is re-architecting the system to support a significantly larger customer base. You will own performance and cost-efficiency across pipelines and services, keeping reliability and operating costs under control as the platform scales.
Build across the stack- This is not a pipelines-only role. You will also write backend services and some frontend, including the internal backoffice the team runs on. We hire builders, not narrow specialists.
Own the core data tables- Own schema design and evolution, data contracts, and the modeling standards the team follows - naming, shared dimensions, normalization, documentation. Be accountable when a table is wrong, late, or drifting.
Level up the teams data work- Pair with and advise software engineers and data scientists on Spark, SQL, and modeling, and help turn notebook-grade code into production-grade pipelines.
Partner cross-functionally- Translate product, client, compliance, and business requirements into clear technical designs and dependable production systems.

Required Technical Lead, Cloud TPU
About the job
Our software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to our needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
This role is a unique leadership opportunity to technically lead by example an engineering team dedicated to critical Cloud Tensor Processing Unit (TPU) software services, which empower our global AI customers. As a Technical Lead (TL), you will lead the architecture and execution of large-scale ML infrastructure, requiring a deep understanding of Large Language Model (LLM) operations from chip level to fleet levels. You will be responsible for solving complex ML infrastructure issues, driving technical strategy, fostering a high-performance team culture, and working directly with customers to ensure successful landings.
Cloud accelerates every organizations ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage our cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
Act as the crucial bridge between raw Tensor Processing Unit (TPU) silicon and production-ready machine learning, owning the software integration and operational ecosystem that powers our most advanced AI.
Lead the end-to-end New Product Introduction process-coordinating complex cross-functional launches from initial concept to General Availability-while ensuring the reliability and scalability of a massive fleet of TPU chips.
Drive foundational engineering efforts, such as developing the TPU runtime API, qualifying the OS images for TPU Virtual Machines (VMs) and Bare Metal instances, and managing fleet-wide reliability through advanced telemetry and automated repair workflows.
Lead the architecture, technology and the overseeing of implementation of the TPU solutions to production.
Analyze customers issues (working with customers and the field team) and translate these into viable technical solutions.

Were looking for an experienced and passionate Data Group Tech Lead, Staff Engineer to join our Data Platform group in TLV. As the Groups Tech Lead, youll shape and implement the technical vision and architecture while staying hands-on across three specialized teams: Data Engineering Infra, Machine Learning Platform, and Data Warehouse Engineering, forming the backbone of our companys data ecosystem.
The groups mission is to build a state-of-the-art Data Platform that drives our company toward becoming the most precise and efficient insurance company on the planet. By embracing Data Mesh principles, we create tools that empower teams to own their data while leveraging a robust, self-serve data infrastructure. This approach enables Data Scientists, Analysts, Backend Engineers, and other stakeholders to seamlessly access, analyze, and innovate with reliable, well-modeled, and queryable data, at scale.
We believe three things matter for every role at our company: drive to push through challenges, efficiency that keeps standards high while moving fast, and adaptability that lets you pivot with data and AI insights. These aren't buzzwords, they're how we actually work.
Our AI-first approach isn't just a tagline either. We're building the future of insurance with AI at the center, and we need people who are genuinely excited to learn and grow alongside these tools.
In this role youll :
Technically lead the group by shaping the architecture, guiding design decisions, and ensuring the technical excellence of the Data Platforms three teams
Design and implement data solutions that address both applicative needs and data analysis requirements, creating scalable and efficient access to actionable insights
Drive initiatives in Data Engineering Infra, including building robust ingestion layers, managing streaming ETLs, and guaranteeing data quality, compliance, and platform performance
Develop and maintain the Data Warehouse, integrating data from various sources for optimized querying, analysis, and persistence, supporting informed decision-makingLeverage data modeling and transformations to structure, cleanse, and integrate data, enabling efficient retrieval and strategic insights
Build and enhance the Machine Learning Platform, delivering infrastructure and tools that streamline the work of Data Scientists, enabling them to focus on developing models while benefiting from automation for production deployment, maintenance, and improvements. Support cutting-edge use cases like feature stores, real-time models, point-in-time (PIT) data retrieval, and telematics-based solutions
Collaborate closely with other Staff Engineers across our company to align on cross-organizational initiatives and technical strategies
Work seamlessly with Data Engineers, Data Scientists, Analysts, Backend Engineers, and Product Managers to deliver impactful solutions
Share knowledge, mentor team members, and champion engineering standards and technical excellence across the organization.

We are seeking an experienced DevOps Engineer to join our Engineering team and play a key role in building and operating our cloud-native platform. The ideal candidate will have hands-on experience managing production environments at scale, driving cloud transformation initiatives, and supporting the of enterprise systems from on-premises deployments to modern SaaS and cloud-native architectures. You will be responsible for designing, automating, and maintaining scalable infrastructure, CI/CD pipelines, and deployment processes that support both our core products and emerging AI-driven capabilities. Working closely with Engineering, QA, Product, and AI teams, you will help ensure the reliability, security, and performance of our services while driving operational excellence and continuous improvement across our technology stack.
Responsibilities:
Design, implement, and maintain CI/CD pipelines.
Manage and optimize cloud infrastructure across AWS, Azure, and/or GCP.
Develop and maintain Infrastructure as Code using Terraform.
Manage Kubernetes-based environments and GitOps deployment workflows using Argo CD and Kustomize.
Lead and support the migration of enterprise applications and infrastructure from on-premises
environments to scalable SaaS and cloud-native architectures.
Establish, maintain, and continuously improve production environments, ensuring high availability, security, scalability, and operational excellence.
Demonstrate strong production ownership, including incident management, root cause analysis, capacity planning, and performance optimization.
Collaborate with Engineering, QA, Product, and AI teams.
Support the deployment, operation, and monitoring of AI and Generative AI services.
Build and maintain monitoring, logging, and alerting systems.
Troubleshoot and resolve infrastructure, deployment, and production issues.

We are looking for an experienced full-stack team lead to join our R&D organization, lead the design and development of large-scale, data-oriented applications.
In this role, you will manage a talented team of full-stack engineers while remaining hands-on and technically involved in building our core SaaS security platform.
You will play a key role in shaping system architecture, driving technological innovation, and ensuring the delivery of robust and scalable solutions in a fast-paced, dynamic environment.
Key Responsibilities:
Lead and mentor a team of full-stack engineers, guiding them in design, development, and delivery of complex projects.
Drive the architecture and implementation of data-centric applications using modern technologies and cloud infrastructure.
Lead a fast-moving development team, ensuring strong execution and teamwork in a dynamic environment
Collaborate with QA, Product, Design, and DevOps teams to ensure system scalability, reliability, and performance.
Conduct code reviews, enforce best practices, and foster a high-quality engineering culture.
Research and integrate new technologies to overcome technical challenges and improve productivity.
Own full project lifecycle, from requirement gathering and design to deployment and monitoring in production.