Machine Learning Engineer - AI Security Team

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Lenovo is a US$83 billion revenue global technology powerhouse, ranked #153 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY).

This transformation together with Lenovo’s world-changing innovation is building a more inclusive, trustworthy, and smarter future for everyone, everywhere. To find out more visit www.lenovo.com, and read about the latest news via our StoryHub.

Candidate must be willing to work onsite three days a week in our Tel-Aviv, Israel office. ***  

You will work at the intersection of software engineering, AI, machine learning, security, and platform engineering, collaborating closely with AI security researchers and engineers to turn emerging AI risks and research into robust, scalable production controls.

The ideal candidate is a strong software engineer with an interest in AI security. Experience in ML, AI, security, AI security, or MLOps is a strong advantage.

Job Responsibilities

• Design, develop, and productionize new capabilities for Lenovo’s AI Controls Platform.
• Translate AI security research, emerging threats, and requirements into practical software capabilities and production controls.
• Design and maintain ML training and fine-tuning pipelines for LLMs and ML models.
• Build scalable, reliable, and efficient services, libraries, pipelines, and tooling for AI security and ML-side capabilities (e.g., dataset labeling, training instrumentation).
• Develop controls and detection capabilities across the AI lifecycle, including data, models, inputs, outputs, and AI agents.
• Integrate security, safety, privacy, robustness, and Responsible AI capabilities into AI development and deployment workflows.
• Build and maintain automated testing, evaluation, CI/CD, monitoring, and observability for AI controls.
• Work closely with engineers, data scientists, security researchers, and product teams to take capabilities from research and prototyping through production deployment.
• Optimize AI controls for real-world production environments, including cloud/on-premises, and edge/on-device environments.
• Investigate emerging AI security challenges and contribute to the design and implementation of new defensive capabilities.
  
  

• 5+ years of professional experience in software engineering, ML engineering, security engineering, or a related technical field.
• BSc or MSc in computer science or a related field, with an MSc preferred.
• Strong programming experience in Python and/or another modern programming language such as Java, Kotlin, C++, Rust, or Go.
• Experience building and deploying production-quality software, services, libraries, or platforms.
• Strong software engineering fundamentals, including testing, debugging, version control, CI/CD, and system design.
• Ability to work effectively across engineering, research, and security teams.
• Strong problem-solving skills and curiosity about AI and security.
  
  

• AI/ML: ML development, model training, fine-tuning, inference, or AI application development.
• MLOps: ML platforms, model lifecycle management, experiment tracking, model deployment, data processing, data pipelines, or ML pipelines.
• AI Security: adversarial ML, LLM security, AI red teaming, AI threat modeling, or AI safety.
• Security: cybersecurity, application security, secure software development, or security controls.
• Agentic AI: AI agents, agent harnesses and orchestrations, tool/function calling, MCP, multi-agent workflows, agent memory and context management, and integration of security controls across agentic workflows.
• Cloud Infrastructure: AWS, Azure, GCP, containers, Kubernetes, or distributed systems.
• Low-Level Software Development: Experience with systems programming, performance optimization, memory management, or native development.
  
  

• Opportunities for career advancement and personal growth
• Access to a diverse range of training programs to sharpen your skills
• Performance-based rewards that celebrate your achievements
• Enjoy the best of both worlds with our hybrid setup: 3 days connecting with the team in the office and 2 days working from the comfort of your home