דרושים » תוכנה » Senior macOS Developer - Workforce AI Security REF257Z

We are on a mission to secure the AI transformation that every company is going through, building a platform that protects organizations as they adopt AI tools at scale. This is your chance to join one of the fastest-growing areas in our company and develop technology that will define how cybersecurity operates in the AI era. You will be part of a multidisciplinary team of backend engineers, endpoint developers, AI researchers, and product experts from Israel and global acquisitions, all working together to deliver cutting-edge, end-to-end security solutions to the worlds largest enterprises.
As a Software Developer on our team, you will work across both backend services and endpoint components, designing scalable systems that connect endpoint-level data collection with cloud-based analysis and enforcement. Youll play a key role in building the agents and services that monitor and control interactions with AI tools, shaping a new category of security products that operate seamlessly across platforms and environments.
Key Responsibilities
Develop and maintain backend services alongside endpoint components for a cross-platform security platform
Design scalable systems that integrate endpoint-collected data with cloud-based processing and enforcement
Build and enhance endpoint agents responsible for monitoring and controlling interactions with AI tools (e.g., coding assistants, web-based AI services)
Implement data pipelines, policy enforcement logic, and secure data handling mechanisms across endpoint and backend layers
Collaborate with AI and product teams to deliver end-to-end solutions
Troubleshoot and resolve complex issues across distributed system and endpoint components.

We're looking for a bright and passionate Senior Software Engineer to join our Endpoint Data Protection team. In this critical role, you will design, develop and maintain core components of our endpoint clients running on Windows and Mac, used by hundreds of thousands of users worldwide, across households and enterprise environments. You will help take our clients to the next level - building new low-level capabilities at the heart of how people move, share and protect their files, devices and network traffic. The scope is broad and the room to grow is real: ownership and technical leadership scale with you.

Responsibilities:

Developing core low-level components for our clients on Windows and Mac
Researching new technologies, including ways to use AI to accelerate engineering and product capabilities
Taking ownership on new features and leading collaborative efforts end to end
Supporting the entire application lifecycle, including release management and supporting production
Collaborating with quality assurance, technical support, product management and other engineering teams to deliver integrated features

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.

Here at our company, we're looking for an innovative, experienced Software Developer with rich experience in web technologies and a strong foundation in modern AI development, who is comfortable with both frontend and backend programming.
As a developer in the Cyber Security Protection area, you will be responsible for agile product development from concept to deployment. You will maintain the operational workflow of critical platforms while integrating autonomous AI systems that impact multiple product services, leading our companys security offering.
You will practice the most advanced client and server-side techniques, alongside cutting-edge AI frameworks, to design and implement both internal and external solutions.
Key Responsibilities
End-to-end backend and frontend development of essential, fast-growing platforms.
Develop and integrate knowledge-based, agentic AI systems capable of reasoning over security data and executing tools autonomously.
Rapidly deliver high-quality, efficient, and productive solutions for internal and external applications.
See through a project from initial plan to completed product, including multi-agent infrastructure orchestration.
Ensure ongoing operation and responsiveness of high-impact applications.
Investigate, implement, and share new technologies (such as LLMs) and ideas to improve scale and performance
Be part of an organic Scrum team developing in a fast-paced environment based on CI/CD.

We're looking for a Tech Lead Engineer to drive the technical strategy for our cross-platform endpoint agent - built in C#/.NET and hardened for production across macOS, Windows, and Linux. You'll own features end to end across a managed fleet of enterprise devices, stay hands-on writing code, and raise the technical bar for a distributed engineering team. This role also means setting the standard for how the team uses AI coding agents responsibly - fast, but production-grade.

What Youll Do
Responsibilities:
Drive the technical strategy for our cross-platform endpoint agent, focusing on C#/.NET core logic while ensuring production readiness across macOS, Windows, and Linux.
Provide technical leadership to a distributed team of engineers, raising technical expertise and engineering practices across the organization.
Own features from discovery to delivery, solving complex domain problems across a managed fleet of enterprise devices.
Maintain a hands-on approach by writing code and mentoring engineers to elevate the organization's technical capabilities.
Architect the agent using C# and platform-specific extensions, ensuring performant integration with each operating system's security frameworks.
Own high-impact technical decisions that balance velocity, security, and reliability.
Deliver maintainable features supported by comprehensive testing, metrics, and monitoring.
Shape the agent roadmap and ensure resilient production systems in collaboration with cross-functional leadership.
Participate in design and code reviews to maintain high engineering standards.
Champion AI-assisted engineering across the team - set the bar for how coding agents accelerate delivery, and establish the practices and guardrails that keep output production-grade.

we are looking for a Software Engineer.
Responsibilities:
Join the Cyber Engineering team, part of At-Bay's Data department, collaborating closely with Cyber Researchers, Data Scientists, Data Engineers, and R&D teams to build the technology behind our cyber insurance products.
Design, build, and scale backend systems that power both customer-facing products and internal security and insurance tools - work that is actively used by our insurance teams and customers every day.
Leverage AI throughout your work - both as an integral part of your development workflow and as a core component of the production solutions you build.
Own challenging, business-critical software problems end to end: from design through deployment, monitoring, and iteration.
Write well-tested, organized, and robust code that supports a rapidly growing, international organization.
Work in a modern engineering environment with continuous integration and deployment on cloud-based infrastructure.
Take full ownership of your code - respond quickly to bugs and ensure new and existing services run smoothly in production.
Communicate openly and proactively with engineers across teams to solve problems, share knowledge, and deliver high-quality software with fast turnaround.

Appdome's mission is to protect every mobile app in the world and the people who use them. We are the leader in AI-native mobile business protection, providing cyber and fraud teams with an agentic platform that builds, monitors, and maintains security defenses in Android and iOS apps — with no SDKs, no coding, and no disruption to engineering cycles.
Our platform delivers over 400 security, anti-fraud, anti-bot, and API protection capabilities, powered by deep learning models trained on a decade of mobile defense data and trillions of live threat events. From build time to runtime, Appdome's AI Agents help mobile brands detect, investigate, and respond to threats faster than ever — recognized as the best AI Platform for Cyber Resilience at RSA Conference 2026 for the second consecutive year. Leading financial, healthcare, m-commerce, and B2B brands rely on Appdome to secure over 50,000 mobile apps and protect more than 1 billion end users globally.
Appdome is an Equal Opportunity Employer. We are committed to diversity, equity, and inclusion in our workplace. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other characteristic protected by law. All qualified applicants will receive consideration for employment without regard to any of these characteristics.
About the Role We are looking for a talented Software Engineer to join Appdome's Identity Group. You will take part in building advanced fraud detection capabilities, working closely with security researchers and data teams to develop cutting-edge solutions that protect hundreds of millions of mobile devices.
Responsibilities
* Design and develop core components of fraud detection systems from early prototyping through production-ready releases.
* Build scalable backend services and distributed systems that process data at scale.
* Work with large-scale datasets and collaborate with data teams to create actionable signals and features.
* Write high-performance, production-quality code in C and C++ optimized for reliability and performance.
* Take ideas from rapid proof-of-concept through to production, tackling complex engineering challenges across the full development lifecycle.

We are hiring Deployment Strategists - Strategic Builders who are customer-facing advisors, who define AI strategy, shape agentic solutions, and drive measurable adoption for enterprise customers. You'll work as part of a delivery team alongside Forward Deployed Engineers, translating complex business challenges into agentic AI deployments that go live and generate real value. Engagements are focused and time-bound - typically 12 weeks or less - and may span a single strategic account or multiple customers simultaneously. Your impact isn't just getting to deployment. It ensures customers realise lasting, scalable value from the solutions your team builds.

Key Responsibilities

Full Lifecycle Ownership: Lead end-to-end customer engagement across the complete value lifecycle - Identify → Commit → Deploy → Consume - owning outcomes at every stage, not just discovery or deal closure

AI Strategy & Architecture: Define the customer's Agentic AI strategy and roadmap. Lead design workshops, agent design sessions, and jobs-to-be-done analysis. Architect solutions spanning Salesforce's agentic AI platform, Data Cloud, and enterprise systems, with responsible AI governance baked in from the start.

Deployment Oversight & Program Management: Manage engagement delivery end-to-end timelines, stakeholder communication, risk escalation, and progress reporting. You are the operational backbone of your delivery team, ensuring nothing falls through the cracks.

Adoption & Consumption: Own adoption, consumption, and expansion metrics within your engagement scope. Identify blockers early and develop strategies to drive measurable value realization

Delivery Team Coordination: Partner closely with the Forward Deployed Engineers on your team to align strategy with technical execution. Keep the team focused, unblocked, and on track to hit delivery milestones.

AI-Supported Development: You'll be developing real customer solutions supported by leading-edge AI tools: Including

Cursor, Claude, and our coding products like Vibes, to accelerate development.

Customer Stakeholder Management: Serve as the primary customer-facing contact, building trust-based relationships with senior stakeholders and present proposals, decisions and outcomes in terms of business value, not just technical detail.

AI Readiness: Assess customer readiness for AI adoption and guide change management and process transformation to maximise the benefits of intelligent agents.

Product Feedback Loop: Synthesise field insights and translate them into actionable product feedback that influences Salesforce's Agentic AI platform roadmap.

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.