Full Stack Engineer

Job Description

Job Description

About Knightscope

Knightscope (NASDAQ: KSCP) is a security technology company building the nation's first Autonomous Security Force — autonomous machines, AI-driven software, and elite security professionals operating as one integrated managed service. The Company is on a mission to make the United States of America the safest country in the world. One Team. One Force.

About the Role

As Knightscope continues to expand its internal AI capabilities, we are seeking a Full Stack Engineer to own the engineering lifecycle of all GenAI-powered applications built and used by Knightscope employees. This is a hands-on, high-impact role responsible for designing, developing, securing, and maintaining internal AI tools that enhance operational efficiency across the company.

You will serve as the central technical owner ensuring that all internal GenAI applications are built with security, scalability, and responsible AI principles embedded from the ground up. The ideal candidate brings full stack engineering expertise, a deep understanding of the Secure Software Development Lifecycle (SSDLC), and hands-on experience integrating large language models and AI APIs into production applications.

Location Requirement: Full-time, on-site at Sunnyvale HQ (No relocation provided)

 

Key Responsibilities

GenAI Application Ownership

• Serve as the technical owner and primary maintainer of all internal GenAI/AI-powered applications across the company, spanning departments such as Operations, Sales, Finance, HR, and Engineering.
• Maintain a centralized registry of all employee-built GenAI tools, ensuring visibility, governance, and version control across all applications.
• Evaluate, onboard, and integrate third-party AI APIs (e.g., OpenAI, Anthropic, Google Vertex AI) and open-source models into internal tooling.
• Partner with cross-functional stakeholders to scope, prioritize, and deliver new AI-powered features and tools that address real business needs
  

Full Stack Development

• Design and build scalable, maintainable front-end interfaces (React, Next.js, or equivalent) for internal AI tools and dashboards.
• Develop and maintain back-end APIs, microservices, and data pipelines (Python, Node.js, or equivalent) to support AI model inference, data retrieval, and workflow automation.
• Architect and manage cloud infrastructure (AWS, GCP, or Azure) to host and scale GenAI applications reliably and cost-effectively.
• Implement RAG (Retrieval-Augmented Generation) pipelines, vector databases, and prompt engineering frameworks to enhance AI application performance.

Secure Software Development Lifecycle (SSDLC)

• Champion and enforce SSDLC best practices across all GenAI projects, including threat modeling, secure code reviews, static/dynamic analysis (SAST/DAST), and dependency scanning.
• Design authentication, authorization, and data privacy controls for all AI applications, ensuring compliance with company security policies and applicable regulations.
• Identify and remediate prompt injection vulnerabilities, data leakage risks, and model misuse scenarios specific to LLM-based applications.
• Establish CI/CD pipelines with integrated security gates, automated testing, and deployment guardrails for all GenAI applications.
• Conduct regular security audits and vulnerability assessments of GenAI applications and maintain a risk register for AI-specific threats.

Governance & Standards

• Define and document internal standards for AI application development, including coding standards, API usage policies, prompt guidelines, and data handling requirements.
• Review and approve employee-built AI applications prior to production deployment, enforcing quality, security, and compliance standards.
• Monitor AI application usage, performance, and costs (token consumption, API spend), and provide regular reporting to leadership.
• Stay current with the rapidly evolving GenAI landscape and proactively recommend new tools, frameworks, or approaches that benefit Knightscope.

 

Required Qualifications

Experience & Education

• 4–8 years of full stack software engineering experience, with at least 2 years working directly with AI/ML APIs or LLM-based applications in a production environment.
• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field (or equivalent practical experience).
• Demonstrated experience owning multiple software projects end-to-end, from architecture through deployment and ongoing maintenance.

Technical Skills

• Proficiency in front-end development: React, TypeScript, Next.js, or equivalent modern frameworks.
• Strong back-end skills: Python and/or Node.js; experience with REST and GraphQL APIs; familiarity with FastAPI, Flask, Express, or similar frameworks.
• Hands-on experience with LLM/GenAI integration: OpenAI API, Anthropic Claude API, Langchain, LlamaIndex, or equivalent frameworks.
• Experience with vector databases (Pinecone, Weaviate, pgvector, or equivalent) and RAG architecture patterns.
• Cloud platform experience (AWS, GCP, or Azure): containerization (Docker, Kubernetes), serverless functions, managed databases, and IAM/secrets management.
• Strong understanding of the Secure Software Development Lifecycle (SSDLC), including threat modeling, OWASP Top 10, SAST/DAST tooling, and secrets management.
• Familiarity with AI-specific security risks: prompt injection, data exfiltration, model inversion, and jailbreak mitigation strategies.
• Proficiency with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, or equivalent) and DevSecOps practices.
• Solid understanding of database design: relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis).

Preferred Qualifications

• Security certifications such as CSSLP, CompTIA Security+, AWS Security Specialty, or equivalent.
• Experience building internal developer platforms, tooling portals, or enterprise AI application hubs.
• Background in robotics, IoT, or physical security technology environments.
• Familiarity with AI governance frameworks, responsible AI principles, and emerging regulations (e.g., EU AI Act, NIST AI RMF).
• Experience with observability tooling (Datadog, Grafana, OpenTelemetry) and monitoring LLM application performance and costs.

 

Compensation & Benefits

• Base Salary: $175,000 – $200,000 (DOE)
• Equity: Stock options
• Benefits: Medical, dental, vision, 401(k), paid time off
• Location Requirement: Full-time, on-site at Sunnyvale HQ