Senior Data Scientist
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 looking for a hands-on Senior Data Engineer / Data Scientist to design and implement data-driven and AI-powered systems that enhance our offensive security capabilities. You’ll build scalable pipelines, deploy intelligent agents, and apply Generative AI, retrieval-augmented generation (RAG), and predictive modeling to solve complex security challenges. This role is ideal for someone who thrives at the intersection of AI innovation, data engineering, and cybersecurity, and wants to shape the next generation of intelligent offensive security tools on top of our MCP server and related platforms.
Requirements
- 5+ years of experience in Data Science, Machine Learning Engineering, or Data Engineering.
- Deep experience with Python, AWS services (S3, Lambda, Batch, Glue, Bedrock, Step Functions, Redshift), and ML frameworks (Scikit-Learn, XGBoost, PyTorch, etc.).
- Proven experience building end-to-end ML pipelines — from data ingestion to model deployment and monitoring.
- Strong understanding of LLM technologies, RAG architectures, and API integration with AI systems.
- Ability to design and manage data architectures for large-scale, multi-tenant environments.
- Experience applying ML or automation to security or operational intelligence domains.
- A builder’s mindset — passionate about shipping scalable, practical AI systems.
Nice To Haves
- Knowledge of offensive security workflows (bug bounty, vulnerability research, red teaming).
- Familiarity with vector databases, embedding models, and semantic search.
- Experience deploying AI solutions in regulated environments (FedRAMP, SOC2).
- Bachelor’s or Master’s in Computer Science, Information Systems, or related field.
Responsibilities
- Design, develop, and deploy LLM- and RAG-powered applications that enhance analyst and hacker productivity across offensive security use cases.
- Integrate Generative AI models (e.g., via AWS Bedrock, OpenAI, Anthropic) with internal APIs and security datasets to automate and augment workflows.
- Build and fine-tune ML models for vulnerability prediction, triage prioritization, and exploit pattern detection.
- Develop evaluation pipelines and feedback loops to continuously improve AI model performance and explainability.
- Architect and maintain large-scale, high-performance data pipelines to process vulnerability, asset, and activity datasets from multiple sources.
- Build secure data ingestion, transformation, and storage workflows leveraging AWS (Glue, Lambda, Step Functions, S3, Redshift, Bedrock) and modern MLOps practices.
- Develop robust CI/CD pipelines for data and ML model deployment using AWS CDK and testing frameworks.
- Partner with infrastructure teams to scale AI workloads efficiently and securely across multi-tenant environments (FedRAMP, SOC2).
- Collaborate with security researchers and engineers to translate offensive security workflows into data-driven automation.
- Integrate ML and AI systems with core security platforms such as the MCP server, Bugcrowd Connect, and vulnerability intelligence pipelines.
- Design APIs and interfaces that enable LLM agents to interact with internal systems for search, enrichment, and decision support.
- Work cross-functionally with data, product, and platform teams to drive adoption of AI capabilities across the engineering organization.
- Provide technical mentorship and guide best practices for ML infrastructure, feature engineering, and model observability.
- Contribute to architectural reviews, ensuring scalability, maintainability, and security in all AI and data systems.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
