Full Stack Developer

Company Overview

Birdseye’s mission is to help make the world a safer place!

We are the leader in AI-driven Remote Facility Supervision solutions for the Trucking and Logistics Industry. Our Maximum Telepresence Approach™ delivers cutting-edge, data-driven security and operational insights that create a safer working environment for millions of people every year.

We provide end-to-end, cutting-edge hardware and AI-driven solutions, redefining security beyond traditional gatehouses. By empowering our professionally trained monitoring agents, we achieve 99.99% reporting accuracy in mission-critical security, safety, and operational support
-24/7/365- for some of the world’s largest logistics facilities, as well as small and medium-sized businesses.

If you're ambitious, thrive in a fast-paced environment, and align with our ICARE values, Birdseye will be the perfect fit for you. If you’re wondering what the world will look like in 10 years - join our Team!

We are looking for a Full Stack Developer who can build production software across the front end and back end.

This needs to be an AI-native developer. We are not looking for someone who has merely tried ChatGPT, Copilot, or Claude. You should have fundamentally changed how you develop software using agentic tools such as Claude Code, Cursor, or equivalent tools. The candidate must be strong with React and Redux on the front end and Java with Spring Boot on the back end.

You should also be productive with either Node.js or Python.

Strong production experience with:

• React
• Redux
• Responsive web applications
• Component-based interface design
• Complex state and workflow management
• API integration
• Automated front-end testing

React and Redux depth is particularly important. The candidate should understand how to keep state, components, and user workflows maintainable as an application grows.

Strong production experience with:

• Java
• Spring Boot
• REST APIs
• Service and business-logic design
• Relational databases
• Authentication and authorization
• Integration and automated testing
• Production troubleshooting

You should also be productive with at least one of:

• Node.js
• Python

Java and Spring Boot remain the primary back-end requirement. Node.js or Python expands the candidate's ability to build supporting services, automation, developer tooling, tests, and integrations.

This is a must-have, not a nice-to-have.

The candidate should use AI throughout the entire software-development lifecycle, including:

• Turning requirements into specifications
• Exploring and understanding existing codebases
• Evaluating architecture and implementation options
• Writing and refactoring code
• Reviewing code and pull requests
• Creating automated tests
• Performing QA
• Reproducing and debugging defects
• Writing technical documentation
• Preparing and validating releases

Your AI toolbox should extend beyond autocomplete inside an IDE. You should be comfortable using agentic command-line tools, repository-level instructions, automated browser and test tooling, and other development capabilities outside the traditional IDE.

You should understand how to:

• Break substantial work into bounded assignments for subagents
• Run independent investigations or implementation tasks in parallel
• Give each agent the right context and constraints
• Manage dependencies between agents

Reconcile multiple outputs into one coherent implementation

• Identify conflicting assumptions or missing integration points
• Manage the context window deliberately
• Summarize long-running work without losing important decisions
• Recognize when context has become stale or overloaded
• Validate AI-produced work before it reaches production

The candidate remains accountable for everything that AI produces. AI mastery does not replace technical judgment.

• Front-end implementation:
  Build clear, responsive React and Redux interfaces for complex operational workflows.
• Back-end implementation:
  Build APIs, integrations, and business logic using Java and Spring Boot.
• AI-native execution:
  Use agents throughout specification, implementation, review, testing, debugging, and documentation.
• Automated quality:
  Create meaningful unit,…