What You’ll Do
- 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.
What We’re Looking For
- 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
- 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.
Nice to Have
- Background in applied research or experience contributing to open-source AI/ML projects.
- Familiarity with compliance and regulatory requirements for AI systems.
- Colorado based - our headquarters are in Denver and while we primarily work from home we have lots of fun
- We’re headquartered in Colorado and prefer to hire locally, but we welcome strong candidates from anywhere in the U.S.
The Goods
- Competitive Compensation: Earn a competitive salary and get an equity stake in the company that we are building together.
- Solid Benefits: Health, dental, and vision insurance 100% paid for employees and dependents. Other benefits include life insurance, AD&D, and 401K
- Time to Recharge Encouraged: Take what you need, vacation plus ten paid holidays! Unplug, recharge, and come back refreshed
- Fun Team and Perks: We do great work and have fun doing it! We take care of our employees… We’ll contribute to your WFH setup and hook you up with occasional at-home perks.
- Opportunity to shape the future of AI-powered features in a high-impact environment.
- Work alongside a talented, collaborative team at the forefront of applied AI.
Top Skills
What We Do
StackHawk is a Series B software-as-a-service (SaaS) company that was founded in 2019. The company's mission is to empower developers to find, tirage, and fix application security bugs in CI/CD, before they hit production.
Why Work With Us
[Product to be Proud Of] Build a product that you want to use yourself. Created for devs to help them deploy secure applications.
[Impact of Your Work] As a fast moving software startup, your daily contributions will show.
[Team You Want to be On] Collaborate to solve difficult problems, learn from each other, and laugh a lot in the process.
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