Manager, Engineering - AI and Data Team Manager

Bugcrowd

3d•$172,000 - $236,500•Remote

About The Position

Bugcrowd is seeking a hands-on Manager for their 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 offensive security capabilities. The 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. 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 Bugcrowd's 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.
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-driven software 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
Master’s degree or higher in Computer Science, Information Systems, or a related quantitative field.

Responsibilities

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.
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.
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.
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.
Architect, govern, and optimize large-scale, high-performance data pipelines and overall software architecture essential for securely and efficiently processing massive vulnerability, asset, and activity datasets sourced from various environments.
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).
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.
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.
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.