AI Software Engineer

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This range is provided by Stack Hawk. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

$/yr - $/yr

The salary range for this position is $150,000 - $190,000.

Stack Hawk is reimagining App Sec for AI-driven development, where applications are built faster than App Sec tools can keep up. We enable App Sec teams to prioritize testing and fixing what matters with our App Sec Intelligence Platform, which combines scalable runtime testing with attack surface discovery from source code. By delivering real‑time visibility and centralized program intelligence, Stack Hawk empowers App Sec teams to evaluate risk, prove value, and scale testing coverage.

The reality: AI has accelerated development velocity by 3-5x, enabling code to be written faster than App Sec teams can track it, while threat actors launch attacks at machine speed. Meanwhile, security teams are stuck with tools that can’t keep up—SAST creates alert fatigue, legacy DAST catches issues too late, and there’s no visibility into what apps exist or how secure they are.

Security can’t just keep up with AI innovation—it has to fuel it.

Are you an experienced AI engineer eager to design and ship cutting‑edge AI systems? Do you want to shape the future of developer‑focused security tooling? Can you tolerate ridiculous bird puns? If so, keep reading to learn about an exciting opportunity at Stack Hawk!

Join Stack Hawk’s expanding Engineering team as a talented AI Software Engineer! You’ll collaborate with our skilled Product Development team to architect, build, and operate production‑grade AI features. This pivotal role directly contributes to Stack Hawk’s mission of revolutionizing application security by building agentic (multi‑prompt, iterative) AI pipelines, enabling intelligent automation, and delivering reliable, safe, and performant AI capabilities  you’re passionate about bringing state‑of‑the‑art AI into production with rigorous engineering practices, this is your opportunity to make a significant impact.

• Design & Build AI Systems:
  Architect and ship Agentic (multi‑prompt, iterative) AI pipelines that solve real‑world problems at scale
• Productionize AI Features:
  Bring AI prototypes into robust, production environments, ensuring reliability, performance, and security
• Advance AI Capabilities:
  Apply state‑of‑the‑art techniques including fine‑tuning, transformer models, retrieval‑augmented generation (RAG), and model evaluation
• Ensure Quality & Safety:
  Implement data guardrails, fairness/bias mitigation strategies, and guardrail systems to ensure safe and reliable model outputs
• Collaborate Across Functions:
  Partner with product, development, and infrastructure teams to deliver high‑impact features
• Operate at Scale:
  Monitor and maintain deployed AI systems using real‑time observability
• Set Standards:
  Establish best practices for testing, evaluation, and continuous improvement of AI/ML features

• Deep AI/ML Expertise
• 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 working with frontier models from OpenAI and Anthropic, including token‑based inference and orchestration
• Software Engineering Excellence
• 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)
• Strong background in scalability, observability, and distributed systems
• Operational Rigor
• Experience deploying and operating AI systems in production
• Familiarity with CI/CD pipelines, automated testing, and evaluation frameworks
• Commitment to reliability, cost efficiency, and user trust
• Mindset
• Quali…