StackHawk helps software developers find and fix security vulnerabilities before they deploy code to production. In the modern world of FinTech, HealthTech, cloud analytics and AI platforms, customers are entrusting their most critical data to software providers. Periodic manual security testing by an external team is simply too risky. Because of this, modern software development organizations are extending CI/CD to encompass Continuous Application and API Security Testing. This way, security can “shift left,” meaning vulnerabilities can be detected while the developer is actively working on the code. StackHawk leads the market in developer first API security testing. Application and API security is an exciting market, where more than 43% of global decision makers are looking to implement dynamic application security testing during software development, which represents a massive opportunity for StackHawk. StackHawk is building out a team of leaders and team members that will capitalize on market pull, and enable growth-phase scale of the business.
Requirements
- Strong foundation in transformer architectures (encoder-only, decoder-only, and multimodal; e.g., BERT, GPT, LLaMA, Mistral).
- Hands-on experience with frontier model tools such as tool calling, MCP, context management, prompt tuning, and evaluation frameworks.
- Knowledge of retrieval systems, embeddings, semantic search, and orchestration of complex AI workflows.
- Experience with NLP tasks: summarization, entity extraction, dialogue systems, or semantic understanding.
- Experience with frontier models from OpenAI and Anthropic, including token-based inference and orchestration.
- Proficiency in Rust or similar high-performance languages (Go, C++, systems-level Python).
- Experience building production-grade services in cloud-native environments (AWS, GCP, or Azure).
Responsibilities
- Architect and ship Agentic (multi-prompt, iterative) AI pipelines that solve real-world problems at scale.
- Bring AI prototypes into robust, production environments, ensuring reliability, performance, and security.
- Apply state-of-the-art techniques including fine-tuning, transformer models, retrieval-augmented generation (RAG), and model evaluation.
- Implement data guardrails, fairness/bias mitigation strategies, and guardrail systems to ensure safe and reliable model outputs.
- Partner with product, development, and infrastructure teams to deliver high-impact features.
- Monitor and maintain deployed AI systems using real-time observability.
- Establish best practices for testing, evaluation, and continuous improvement of AI/ML features.
Other
- Collaborate with our skilled Product Development team to architect, build, and operate production-grade AI features.
- Quality-minded, with a deep respect for correctness.
- Comfortable working at the intersection of research and production engineering.
- Collaborative and impact-driven, with a track record of influencing technical direction.
- Background in applied research or experience contributing to open-source AI/ML projects.
