Manager, Engineering - AI and Data

About Bugcrowd

We are Bugcrowd. Since 2012, we’ve been empowering organizations to take back control and stay ahead of threat actors by uniting the collective ingenuity and expertise of our customers and trusted alliance of elite hackers, with our patented data and AI-powered Security Knowledge Platform™. Our network of hackers brings diverse expertise to uncover hidden weaknesses, adapting swiftly to evolving threats, even against zero-day exploits. With unmatched scalability and adaptability, our data and AI-driven CrowdMatch™ technology in our platform finds the perfect talent for your unique fight.

The Role

We’re seeking a hands-on Manager for the AI and Data Science team. This role will manage a high-performing team dedicated to developing data-driven and AI-powered systems that significantly enhance our offensive security capabilities. Your primary focus (70%) will be on setting the technical direction, working with the team in building scalable data pipelines, and training and deploying predictive models to solve complex cybersecurity challenges. This leadership role also includes mentoring and managing the team.

Essential Duties and Responsibilities

• Lead Technical Strategy & Execution: Define and drive the technical roadmap for AI, ML, and data systems, overseeing development, deployment, and operationalization to ensure robust performance, scalability, and direct alignment with business strategy.
• Team Leadership & Mentorship: Lead, mentor, and grow a small high-performing team of data scientists and ML engineers, cultivating a culture of technical excellence, accountability, and continuous learning.
• AI/ML/Generative Systems Development: Direct the entire lifecycle—from design to deployment—of robust data pipelines, scalable model training, and innovative AI/ML applications to boost analyst and hacker productivity.
• Model Development and Platform Integration: Guide development, tuning, deployment, and MLOps of ML models. Ensure secure integration of generative AI models (AWS Bedrock, OpenAI, Anthropic) with internal APIs and sensitive datasets.
• Data and Software Architecture: Architect, govern, and optimize large-scale, high-performance data pipelines and software architecture for processing massive vulnerability, asset, and activity datasets.
• Security & Scalability Partnership: Collaborate with infrastructure teams to architect AI workloads and data pipelines meeting stringent requirements for security, efficiency, and scalability in multi-tenant and regulated environments (FedRAMP, SOC2).
• Cross-Functional Collaboration: Serve as the primary technical subject matter expert, partnering with security research, product, and platform teams.
• Continuous MLOps Improvement: Establish and manage MLOps practices, CI/CD pipelines, evaluation frameworks, and monitoring tools.
• API Design & Integration: Oversee the design of robust APIs and interfaces facilitating seamless interaction between LLM agents and internal systems.

Education, Experience, Knowledge, Skills, and Abilities

• 5+ years of experience in Data Science, ML Engineering, or Data Engineering, with 2+ years in a technical leadership or team lead role.
• Strong architectural understanding of LLM technologies, RAG architectures, prompt engineering, ML Ops, and secure API integration with AI systems.
• Deep expertise with Python, AWS services (S3, Lambda, Batch, Glue, Bedrock, Step Functions, Redshift), and ML frameworks.
• Proven experience successfully leading a team to build and deploy end-to-end ML pipelines.
• Ability to design, manage, and govern secure data-driven software architectures for large-scale, multi-tenant environments.
• Excellent communication skills with a demonstrated ability to mentor engineers and influence technical direction.

Preferred Experience

• Deep knowledge of offensive security workflows (bug bounty, vulnerability research, red teaming).
• Experience deploying and operating AI solutions within regulated environments (FedRAMP, SOC2).
• Experience and knowledge of software security.
• Master’s degree or higher in Computer Science, Information Systems, or a related quantitative field.