Software Engineer - AI Systems
New
X
XBOWCybersecurity
Location: Austria (Remote)
Secondary Locations: Greece (Remote), United Kingdom (Remote), Switzerland (Remote), US East Coast, Malta (Remote), Brazil (Remote), Spain (Remote), Denmark(Remote), Ireland (Remote), Remote Argentina, Sweden (Remote)
Workplace: RemoteFull-Time
Salary$100K - $350K; $100K – $350K • Offers Equity
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Job Details
- Required Skills
- Data AnalysisSoftware ArchitectureTypeScript
Requirements
- Have strong experience building software around LLMs, including prompting, agentic orchestration, and fault tolerance.
- Have experience integrating LLM components with hard-coded logic.
- Have strong software engineering skills in architecting and building reliable, maintainable, production-grade software.
- Have experience with TypeScript or the ability to learn a new programming language quickly.
- Have strong structured and independently driven problem-solving skills.
- Be able to work with incomplete information and rapidly test hypotheses.
- Hold an MSc or equivalent or higher in computer science, math, physics, or machine learning.
- Prior security-sector experience, especially offensive security or applying LLMs in a security context, is a bonus.
- Open-source contributions, particularly involving LLM applications or frameworks, are a bonus.
- A strong theoretical background, such as a Ph.D. in machine learning, computer science, or math, is a bonus.
- Startup experience and comfort with ambiguity are a bonus.
Responsibilities
- Design prompt flows and orchestrations for LLM-powered software.
- Architect and build a production-grade, testable, and maintainable AI software stack.
- Design experiments and evaluation frameworks for performance testing at scale.
- Conduct data analysis to draw conclusions from system performance.
- Collaborate with AI and security experts, as well as frontend and backend developers, to build end-to-end systems.
- Own projects from ideation and experimentation through deployment and production monitoring.
- Research ways to use advances in LLMs to improve system performance.
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