Technical Product Manager for AI Security Products

Technical Product Manager for AI Security Products

Remote [within the US]

ABOUT THE ROLE:

HiddenLayer is entering an exciting new chapter in our company history and we are seeking a Technical Product Manager for our AI Security products to drive the development of a groundbreaking cybersecurity platform to secure and protect against adversarial AI attacks. In this role, you'll have the opportunity to work with industry pioneers in data science and artificial intelligence threat research in a fast-paced emerging market. 

As a Product Manager with a strong background in cybersecurity and AI, you will play a crucial role in the development, enhancement, and delivery of HiddenLayer’s AISec Platform to protect and defend generative AI and agentic systems against cybersecurity threats and attacks.

WHO WE ARE:

HiddenLayer protects the world’s most valuable technologies from adversarial AI attacks. We were founded by AI professionals and security specialists with first-hand experience of how insidious adversarial AI attacks can be to detect and defend against. Determined to prove that these attacks were preventable, the team developed a unique, patent-pending, productized solution to support organizations in accelerating their adoption of AI securely. 

Our dedication to innovation has been recognized by prestigious awards such as RSA's Innovation Sandbox Winner, CB Insights AI 100, CyberTech 100, and SC's Most Promising Early-Stage Start-up.

WHAT YOU’LL DO:

You will be responsible for translating customer needs, technical insights, and the evolving Adversarial AI threat landscape into actionable product capabilities and detection techniques. Your exceptional understanding of both machine learning and cybersecurity will allow you to effectively guide cross-functional teams, bridging the gap between sales engineering, product development, data science, threat research, and professional services.

• Product Roadmap Planning: Manage, maintain, and prioritize HiddenLayer’s backlog of product and feature ideas
• Product Execution: Collaborate with internal stakeholders (engineering, data science, and threat research) to define requirements and deliver high-quality product capabilities and user experience
• Adversarial AI Threat Landscape:  Maintaining vigilance and knowledge of the Adversarial AI Threat Landscape and MITRE ATLAS to define and prioritize detection requirements for HiddenLayer’s products
• Market Analysis: Conduct comprehensive market research and competitive analysis to identify emerging trends, opportunities, and potential threats in the machine learning and cybersecurity landscape
• Customer Insights: Engage with customers and gather feedback to understand their pain points, requirements, and preferences to drive product improvements.
• Requirements Definition: Clearly define product requirements, use cases, and specifications, collaborating with engineering, data science, and threat research teams to ensure the feasibility and effectiveness of solutions

Go-to-Market Launches: Coordinate product launches and develop effective go-to-market strategies, collaborating with marketing, sales, and support teams to drive product adoption and success in the market.

WHO YOU ARE:

• Candidates could be:
• Technical Product Managers in data science or machine learning operations who seek to delve into cybersecurity
• Technical Product Managers in cybersecurity who have data science or AI operations experience
• Former Data Scientists, AI Engineers, or developers who want to make a career pivot into product management
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field.
• In-depth knowledge of AI techniques, algorithms, frameworks, and AI Operation tools.
• Strong understanding of cybersecurity principles, best practices, and industry standards.
• Experience working with cross-functional teams, including engineering, data science, threat research, marketing, sales, and support.
• Proficient in Agile product development methodo