We are seeking a highly skilled, motivated Senior Full-Stack Engineer II to join the Customer Experience Offerings (CXO) team. This team delivers and scales our core offerings and agentic experiences, creating smart automation for customers to confidently understand, configure, and optimize their journey.

What Youll Be Doing
Act as the go-to technical expert across one or more core areas of the CXO platform, setting the standard for code quality, maintainability, and architecture for the team.
Drive technical decision-making on complex, mission-critical systems. Lead design reviews for large-scale initiatives and contribute to all major architectural decisions within your domain.
Own and deliver large, cross-system initiatives, handling open-ended problems with ambiguity. Define key success metrics and measure project impact post-delivery.
Proactively identify and drive improvements to system costs, scale, and operational stability.
Proactively engage with customers to understand their needs, document pain points and suggestions, and drive improvements that benefit multiple teams.
Improve team processes: identify gaps in documentation, design/code review practices, and onboarding; propose and lead adoption of improvements.
Think like a product owner: proactively identify opportunities to improve the customer experience from quality-of-life improvements to impactful new capabilities and bring them to the roadmap.

We are looking for a Senior Software Engineer to join our Core Engineering group. The group is responsible for the real-time, low-latency infrastructure that powers our fraud decisions and external APIs.

Our systems process billions of requests every day, ensuring high availability, security, and performance at global scale.

In this role, youll work on core backend components such as our decision engine, ingestion and enrichment pipelines, schema management systems, and self-serve API platform. The software you build will power critical business decisions and directly serve some of the worlds largest merchants.

This is a high-impact, high-ownership position for an engineer who thrives on solving complex distributed systems challenges, cares deeply about production-grade quality, and wants to shape the foundation of our decisioning platform.


What you'll be doing:
Design, build, and scale backend systems that power our real-time decisioning and APIs.
Own projects end-to-end - from design and implementation to production rollout and monitoring.
Ensure systems are low-latency, fault-tolerant, and high-throughput across distributed environments.
Enhance observability, reliability, and developer experience through strong operational and tooling practices.
Collaborate with Product, analysts, data scientists, and infrastructure teams to drive innovation across Forters decision ecosystem.
Participate in technical discussions and customer interactions, providing expertise and clear communication when supporting enterprise integrations.

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.

Forward Deployed Engineering sits at the frontier of that work. We embed with our most strategic enterprise customers, running on-prem, air-gapped, and heavily regulated environments, and own the technical arc from discovery through production.

Your day might include designing a solution architecture for a high-impact agentic use case, debugging a production routing issue deep in our codebase, deploying a new MCP server with a customer team, or aligning with a CTO on a rollout plan; all in service of outstanding outcomes and rapid time to value for the organizations we work with.

This is a founding function. The first FDEs will define how we conducts field engineering: the standards, playbooks, and technical bar. The work is hands-on, commercially scoped, and high-leverage: youll partner closely with Product, Research, Platform, Security, and GTM, and what you learn in production directly shapes what the core team ships next. If you thrive under ambiguity, make fast and sound decisions when the stakes are high, and get energy from closing the gap between what customers hit in production and what we build next, this role is for you.

In This Role You Will
Own technical delivery across multiple deployments from first prototype to stable production, including core engineering work that unblocks strategic accounts.
Lead deep technical diagnosis and remediation in our codebase and production environments, model routing and gateway behavior, large-PR scaling, multi-pass behavior, performance and reliability issues.
Build customer-specific extensions (MCP servers, agent skills, platform integrations) that deliver immediate value and become durable product capability; upstream them into the main roadmap where appropriate.
Embed closely with customer engineering teams: serve as their senior technical counterpart for hands-on debugging, architecture reviews, and joint implementation.
Scope work, sequence delivery, and remove blockers early; make deliberate trade-offs between scope, speed, and quality to protect delivery timelines.
Partner with Product, Research, and Platform to convert account learnings and eval-driven feedback into roadmap items, specs, and prioritized engineering work.
Codify working patterns into tools, playbooks, runbooks, and reproducible benchmarks that Support and account teams can operate independently and help scale the field engineering function across the organization.

As a Senior Principal DevOps Engineer, you will serve as a visionary technical leader within the Cortex Cloud DevOps group. You will define the technical strategy and architecture that ensures our massive-scale production services remain highly reliable, exceptionally secure, and performant. You will pioneer the integration of AI-driven capabilities into our daily operations, establishing elite engineering standards and fundamentally transforming the workflows of hundreds of developers through autonomous agents and intelligent procedures.
Your Career
Architectural Vision & Scalability: Design and scale massive, resilient distributed systems and global Kubernetes infrastructure, implementing robust observability and monitoring frameworks.
AI-Driven Transformation: Revolutionize the SDLC by integrating Generative AI, autonomous agents, and LLM-powered workflows into CI/CD and self-healing systems to accelerate developer velocity.
Technical Leadership & IaC: Define architectural standards, lead GitOps/IaC (FluxCD/Terraform) strategies, and mentor Senior/Staff engineers across the R&D organization.
Developer Experience & Efficiency: Build and champion AI-powered platforms that automate troubleshooting and eliminate friction. Partner directly with Engineering Directors, Principal Architects, and Product Management to align infrastructure initiatives with business goals, optimizing for scale, high availability, and multi-million-dollar cost-efficiencies.
Security & Compliance: Embed "Security by Design" principles into the platform architecture to ensure platform integrity without sacrificing delivery speed.

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.

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.

As a Staff Software Engineer, you will serve as a technical authority and play a pivotal role in scaling our core platform. Your focus will span architecture, security research, and engineering excellence:

Lead & Architect: Design, build, and scale high-performance software while driving architectural best practices across the R&D organization.
Security & AI Innovation: Drive deep-dive research into cloud, SaaS ecosystems, and AI agents to build proactive detection models, mitigation strategies, and threat intelligence.
Engineering Excellence: Elevate code quality, testing standards, system monitoring, and documentation across teams.
Cross-Functional Ownership: Partner closely with Product and R&D to translate complex security requirements into scalable technical solutions.
Scale & Optimize: Identify and resolve systemic performance bottlenecks and architectural challenges.
Mentorship & Leadership: Act as a technical mentor, driving engineering standards and fostering a strong knowledge-sharing culture.

Key Responsibilities
We are looking for a passionate Software Developer to join our WAF SaaS Platform team, where we build, evolve, and operate our companys cloud-native WAF SaaS product.
In this role, you will develop new SaaS product capabilities while also building and operating the cloud infrastructure that powers the platform. You will work across backend services, Kubernetes environments, automation, CI/CD, monitoring, and, when needed, frontend components.
We are looking for a strong team player with a high sense of ownership, a get-things-done mindset, and the willingness to contribute across all areas of development - from UI and backend to DevOps and cloud infrastructure.
Major Responsibilities
Develop new features and capabilities for our companys WAF SaaS product, with a focus on backend services, cloud infrastructure, and DevOps-oriented development
Design, build, and maintain cloud-native infrastructure that supports customer environments, production workloads, and global SaaS deployments
Take part in creating and operating new POPs, improving customer environment provisioning, and evolving the platforms scalability and availability
Develop SaaS product capabilities such as certificate management, CDN-related flows, DDoS-related capabilities, onboarding flows, and other customer-facing platform features
Work with cloud services, CI/CD pipelines, monitoring, logging, and automation tools
Take end-to-end ownership of features and infrastructure components, from design and implementation through deployment, observability, support, and production operation
Build and maintain dashboards, alerts, and operational tools, using data and metrics to improve reliability, performance, and product decisions
Collaborate closely with developers, DevOps engineers, product managers, security researchers, support teams, and other stakeholders
Support production environments, investigate customer issues, and understand the real-world impact of features, bugs, and infrastructure changes.