LMTS - AI Automation Engineer - Offensive Security

Responsibilities

• Design, implement, and maintain agent-based AI automation workflows under the technical direction of the AI Automation Director
• Build systems capable of:
• Reasoning over large and heterogeneous data sources
• Planning and executing multi-step workflows
• Grounding actions in real-world data
• Result-adaptive execution
• Implement core components, including:
• Agent logic and orchestration layers
• Tool interfaces and execution handlers
• State management, memory, and context handling
• Translate high-level automation designs into robust, testable implementations
• Integrate AI-driven workflows with existing services, data sources, and platforms
• Implement observability, logging, and debugging mechanisms for AI-assisted systems
• Participate in evaluation and iteration of prompts, workflows, and control logic
• Collaborate closely with security practitioners to ensure outputs are actionable and operationally relevant
• Contribute to engineering standards around safety, reliability, and change management
• Required Qualifications
• 9+ years of professional software engineering experience, with strong exposure to AI-driven systems or automation
• Hands-on experience with LLMs and agentic frameworks beyond basic prompt usage
• Strong understanding of:
• Agent orchestration and execution control
• Task planning and decomposition
• State, memory, and context management
• Proven ability to build production-grade systems, not just prototypes
• Strong software engineering fundamentals, including:
• Modular system design
• API integration
• Distributed systems
• Experience working in fast-moving, ambiguous problem spaces
• Experience in offensive security, penetration testing, red teaming, application security, or vulnerability research with demonstrated technical skills
• Ability to reason about failure modes and edge cases in semi-autonomous systems
• Strong communication skills and ability to collaborate closely with senior technical leadership
• Preferred Qualifications (Advantage)
• Experience in security engineering, offensive security, or security research
• Familiarity with:
• Large-scale data ingestion and normalization
• Signal correlation or prioritization systems
• Workflow engines or automation platforms
• Experience with:
• Model evaluation or fine-tuning
• Prompt optimization and structured prompting
• Background in systems that operate across large, evolving external surfaces
• Comfort balancing experimentation with production reliability
• Unleash Your Potential
• Accommodations
• If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form .

Additional Information

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts. Job Category Product Job Details About Salesforce Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword - it's a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all. Ready to level-up your career at the company leading workforce transformation in the agentic era? You're in the right place! Agentforce is the future of AI, and you are the future of Salesforce. We are looking for a Lead AI Automation engineer to design and build advanced AI-driven automation that supports offensive security and security research workflows. This is a deeply hands-on engineering role. You will work closely with the AI Automation Director and a small, agile team to implement orchestrated, agent-based automation-translating architectural direction into reliable, production-grade systems. The focus is on building automation that can reason over complex data, coordinate multi-step actions, and operate safely in real environments. This role is ideal for an experienced engineer who has built real AI systems, understands their limitations, and wants to apply them to complex, adversarial problem spaces.

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