Technical Project Manager

Job Summary

Must-Have Skills

• Technical program management: Proven experience leading highly technical cybersecurity, research, software engineering, AI, or emerging-technology programs from research hypothesis through prototype, proof of concept, testing, benchmarking, recommendation, and operationalization.
• Program execution: Strong capability in roadmaps, prioritization, milestones, dependencies, deliverables, success criteria, risk management, resource planning, delivery tracking, metrics, and cross-functional issue resolution.
• Cybersecurity expertise: Strong knowledge of application security, AI security, vulnerability research, threat modelling, secure development, network security, infrastructure security, and security architecture.
• AI and agentic systems: Strong understanding of LLMs, AI agents, tool calling, orchestration frameworks, APIs, agentic security workflows, AI-enabled security automation, and security harnesses.
• Technical engineering fluency: Ability to work effectively with researchers and engineers across software, models, APIs, security tools, cloud platforms, containers, automation frameworks, and security test environments.
• Programming knowledge: Strong technical understanding of modern software development, including Python and working familiarity with languages such as Java, Node.js, Go, and C/C++.
• Research-to-delivery translation: Demonstrated ability to convert research hypotheses and emerging-technology opportunities into structured workstreams, experiments, prototypes, measurable deliverables, and enterprise recommendations.
• Product and technology evaluation: Experience coordinating hands-on technical evaluations, proofs of concept, benchmarking, vendor assessments, architecture reviews, demonstrations, and technical due diligence.
• Evaluation frameworks: Ability to define requirements, benchmarks, and decision criteria covering security effectiveness, technical capability, scalability, architecture, integration complexity, operational applicability, and enterprise applicability.
• Risk and recommendation skills: Ability to identify product limitations, security concerns, technical risks, integration requirements, and operational-readiness gaps, then produce evidence-based recommendations.
• Stakeholder management: Strong collaboration across cybersecurity engineering, security research, application security, architecture, infrastructure, cloud, vendors, startups, and leadership stakeholders.
• Communication and documentation: Strong technical writing, documentation, presentation, and communication skills, including the ability to explain complex research findings to engineering teams, security leadership, and innovation or investment stakeholders.
• Knowledge transfer: Ability to ensure research findings, source code, prototypes, evaluation artifacts, decisions, risks, and technical knowledge are documented and transferred appropriately.
• Education: Master’s degree in Cybersecurity, Computer Science, Artificial Intelligence, Computer Engineering, or a related technical field.

Key Responsibilities

• Technical program management: Proven experience leading highly technical cybersecurity, research, software engineering, AI, or emerging-technology programs from research hypothesis through prototype, proof of concept, testing, benchmarking, recommendation, and operationalization.
• Program execution: Strong capability in roadmaps, prioritization, milestones, dependencies, deliverables, success criteria, risk management, resource planning, delivery tracking, metrics, and cross-functional issue resolution.
• Cybersecurity expertise: Strong knowledge of application security, AI security, vulnerability research, threat modelling, secure development, network security, infrastructure security, and security architecture.
• AI and agentic systems: Strong understanding of LLMs, AI agents, tool calling, orchestration frameworks, APIs, agentic security workflows, AI-enabled security automation, and security harnesses.
• Technical engineering fluency: Ability to work effectively with researchers and engineers across software, models, APIs, security tools, cloud platforms, containers, automation frameworks, and security test environments.
• Programming knowledge: Strong technical understanding of modern software development, including Python and working familiarity with languages such as Java, Node.js, Go, and C/C++.
• Research-to-delivery translation: Demonstrated ability to convert research hypotheses and emerging-technology opportunities into structured workstreams, experiments, prototypes, measurable deliverables, and enterprise recommendations.
• Product and technology evaluation: Experience coordinating hands-on technical evaluations, proofs of concept, benchmarking, vendor assessments, architecture reviews, demonstrations, and technical due diligence.
• Evaluation frameworks: Ability to define requirements, benchmarks, and decision criteria covering security effectiveness, technical capability, scalability, architecture, integration complexity, operational applicability, and enterprise applicability.
• Risk and recommendation skills: Ability to identify product limitations, security concerns, technical risks, integration requirements, and operational-readiness gaps, then produce evidence-based recommendations.
• Stakeholder management: Strong collaboration across cybersecurity engineering, security research, application security, architecture, infrastructure, cloud, vendors, startups, and leadership stakeholders.
• Communication and documentation: Strong technical writing, documentation, presentation, and communication skills, including the ability to explain complex research findings to engineering teams, security leadership, and innovation or investment stakeholders.
• Knowledge transfer: Ability to ensure research findings, source code, prototypes, evaluation artifacts, decisions, risks, and technical knowledge are documented and transferred appropriately.
• Education: Master’s degree in Cybersecurity, Computer Science, Artificial Intelligence, Computer Engineering, or a related technical field.

Skill Requirements

Security Technical Program Manager Must-Have and Nice-to-Have Skills Must-Have Skills Technical program management: Proven experience leading highly technical cybersecurity, research, software engineering, AI, or emerging-technology programs from research hypothesis through prototype, proof of concept, testing, benchmarking, recommendation, and operationalization. Program execution: Strong capability in roadmaps, prioritization, milestones, dependencies, deliverables, success criteria, risk management, resource planning, delivery tracking, metrics, and cross-functional issue resolution. Cybersecurity expertise: Strong knowledge of application security, AI security, vulnerability research, threat modelling, secure development, network security, infrastructure security, and security architecture. AI and agentic systems: Strong understanding of LLMs, AI agents, tool calling, orchestration frameworks, APIs, agentic security workflows, AI-enabled security automation, and security harnesses. Technical engineering fluency: Ability to work effectively with researchers and engineers across software, models, APIs, security tools, cloud platforms, containers, automation frameworks, and security test environments. Programming knowledge: Strong technical understanding of modern software development, including Python and working familiarity with languages such as Java, Node.js, Go, and C/C++. Research-to-delivery translation: Demonstrated ability to convert research hypotheses and emerging-technology opportunities into structured workstreams, experiments, prototypes, measurable deliverables, and enterprise recommendations. Product and technology evaluation: Experience coordinating hands-on technical evaluations, proofs of concept, benchmarking, vendor assessments, architecture reviews, demonstrations, and technical due diligence. Evaluation frameworks: Ability to define requirements, benchmarks, and decision criteria covering security effectiveness, technical capability, scalability, architecture, integration complexity, operational applicability, and enterprise applicability. Risk and recommendation skills: Ability to identify product limitations, security concerns, technical risks, integration requirements, and operational-readiness gaps, then produce evidence-based recommendations. Stakeholder management: Strong collaboration across cybersecurity engineering, security research, application security, architecture, infrastructure, cloud, vendors, startups, and leadership stakeholders. Communication and documentation: Strong technical writing, documentation, presentation, and communication skills, including the ability to explain complex research findings to engineering teams, security leadership, and innovation or investment stakeholders. Knowledge transfer: Ability to ensure research findings, source code, prototypes, evaluation artifacts, decisions, risks, and technical knowledge are documented and transferred appropriately. Education: Master’s degree in Cybersecurity, Computer Science, Artificial Intelligence, Computer Engineering, or a related technical field. Nice-to-Have Skills Advanced education: PhD in Cybersecurity, Computer Science, Artificial Intelligence, Computer Engineering, or a related technical field. Emerging cybersecurity domains: Exposure to quantum or post-quantum cryptography, blockchain security, and other frontier cybersecurity technologies. Advanced AI security: Experience with AI red teaming, autonomous penetration testing, agent security, AI-native security tooling, or advanced AI frontier models applied to cybersecurity. Cybersecurity innovation: Published research papers, patents, conference presentations, open-source security projects, vulnerability research, or other demonstrated technical innovation. Startup and investment ecosystem: Experience with cybersecurity startups, founding or early-stage products, accelerators, venture capital, corporate venture teams, or technical pitching to investment stakehold

Other Requirements

Nice-to-Have Skills

• Advanced education: PhD in Cybersecurity, Computer Science, Artificial Intelligence, Computer Engineering, or a related technical field.
• Emerging cybersecurity domains: Exposure to quantum or post-quantum cryptography, blockchain security, and other frontier cybersecurity technologies.
• Advanced AI security: Experience with AI red teaming, autonomous penetration testing, agent security, AI-native security tooling, or advanced AI frontier models applied to cybersecurity.
• Cybersecurity innovation: Published research papers, patents, conference presentations, open-source security projects, vulnerability research, or other demonstrated technical innovation.
• Startup and investment ecosystem: Experience with cybersecurity startups, founding or early-stage products, accelerators, venture capital, corporate venture teams, or technical pitching to investment stakeholders.
• Operationalization: Experience transitioning successful cybersecurity research prototypes into production-ready or enterprise capabilities.
• Certifications: CISSP, OSCP, CSSLP, CISM, or equivalent cybersecurity certification.