AI and Data Team Manager

Bugcrowd

14h•Remote

About The Position

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. We aim to create a new era of modern crowdsourced security that outpaces threat actors. Unleash the ingenuity of the hacker community with Bugcrowd, visit www.bugcrowd.com. Based in San Francisco and New Hampshire, Bugcrowd is supported by General Catalyst, Rally Ventures, Costanoa Ventures, and others. 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, guiding 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. This position is ideal for someone who excels in the technical execution of AI and Data engineering and is ready to lead the development of our next-generation, AI-driven preemptive cybersecurity product alongside a group of exceptional engineers.

Requirements

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.
Master’s degree or higher in Computer Science, Information Systems, or a related quantitative field.
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—from data ingestion to model deployment, monitoring, and MLOps.
Ability to design, manage, and govern secure data architectures for large-scale, multi-tenant, and high-security environments.
Excellent communication skills with a demonstrated ability to mentor engineers, influence technical direction, and present complex concepts to both technical and non-technical audiences.

Nice To Haves

Deep knowledge of offensive security workflows (bug bounty, vulnerability research, red teaming) and the associated datasets.
Experience successfully deploying and operating AI solutions within regulated environments (FedRAMP, SOC2).
Experience and knowledge of software security

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 key business strategy and requirements.
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. Focus on leveraging these systems to significantly boost analyst and hacker productivity across critical offensive security use cases.
Model Development and Platform Integration: Guide the development, tuning, deployment, and MLOps of Machine Learning models for cybersecurity. Ensure secure and compliant integration of cutting-edge generative AI models (via platforms like AWS Bedrock, OpenAI, Anthropic) with internal APIs and sensitive security datasets.
Data Architecture Governance: Architect, govern, and optimize large-scale, high-performance data pipelines essential for securely and efficiently processing massive vulnerability, asset, and activity datasets sourced from various environments.
Security & Scalability Partnership: Collaborate closely with infrastructure teams to architect AI workloads and data pipelines that meet stringent requirements for security, efficiency, and scalability, particularly within multi-tenant and regulated environments (e.g., FedRAMP, SOC2).
Cross-Functional Collaboration: Serve as the primary technical subject matter expert and liaison, partnering with security research, product, and platform teams to translate complex offensive security challenges into robust, data-driven automation and intelligence solutions.
Continuous MLOps Improvement: Establish and manage best-in-class MLOps practices, including CI/CD pipelines, comprehensive evaluation frameworks, and robust monitoring/observability tools to ensure the continuous improvement and reliability of all data and AI systems.
API Design & Integration: Oversee the design of robust, high-availability APIs and interfaces that facilitate seamless, scalable interaction between LLM agents and internal systems (such as the MCP server) for crucial tasks like search, data enrichment, and automated decision support.