Software Engineer

As a Full Stack Software Engineer, you’ll deliver high-quality, reliable product features across Axios’ frontend and backend systems, using modern agentic development practices to accelerate delivery and ensuring performance and stability across our publishing and delivery platforms. Key responsibilities include:

• End-to-end feature development: Own features across the frontend, APIs, business logic, data layer, testing, and production systems. Turn product problems into clear technical plans and reviewable units of work, using AI agents thoughtfully across research, prototyping, implementation, testing, refactoring, and documentation.
• Frontend development: Create accessible, responsive, and maintainable interfaces using JavaScript or TypeScript, React, and modern web frameworks such as Next.js.
• Backend and API development: Build production services and APIs that support Axios’ products and publishing workflows, and create clear contracts that can be understood and used reliably by people, applications, and AI-powered tools.
• AI-enabled product development: Identify opportunities where models, agents, retrieval, or intelligent automation can create meaningful value for readers or internal teams. Build those capabilities with clear user controls, measurable behavior, appropriate guardrails, and an understanding of when conventional software is the better solution.
• Quality and reliability: Write automated tests, improve performance, and contribute to logging, metrics, tracing, security, and operational reliability.
• Collaboration: Partner with product, design, quality engineering, and other developers to clarify requirements, explain tradeoffs, and deliver effectively. Participate in code reviews, pairing, onboarding, and mentorship.
• Product and technical improvement: Understand how technical work supports readers, editorial workflows, and business goals.Improve existing systems through thoughtful refactoring, sound technical decisions, and practical experimentation with emerging engineering approaches.

Skills:

The ideal candidate is a thoughtful engineer who cares about user experience, reliability, scalability, and maintainable code—and who has moved beyond occasional AI assistance to using agents as a substantive part of their engineering practice. You should have:

• Experience: 2-5+ years of professional software development experience, with production experience in frontend, backend, or full-stack development and the ability to contribute across the stack.
• AI-native development: Hands-on experience using agentic coding tools—such as Claude Code, Codex, Cursor, GitHub Copilot, or comparable tools—to complete meaningful, multi-step software work, rather than only generating snippets or using autocomplete.
• Agent direction and context: Ability to write clear specifications and acceptance criteria, provide relevant context, break work into appropriately scoped tasks, decide what to delegate, and guide an agent when its initial approach is incomplete or incorrect.
• Verification and engineering judgment: Ability to review AI-generated code critically, validate assumptions against the actual system, identify security and maintainability risks, and recognize when an agent-produced solution is plausible but wrong. You understand that AI increases the importance of sound architecture, testing, and human judgment.
• Frontend development: Experience building production interfaces with JavaScript or TypeScript and React, preferably with Next.js or a comparable modern framework.
• Backend development: Experience building production services and APIs. Our backend systems primarily use Go and Python, and you should be comfortable working in or learning either language.
• Data fundamentals: Experience with relational databases, SQL, schema design, transactions, and migrations.
• Quality and security: Experience with unit and integration testing, accessibility, input validation, authentication, authorization, and common web security practices.
• Engineering judgment: Ability to own scoped work, make sound tradeoffs, navigate ambiguity, and connect technical decisions to product and business goals. You can evaluate when an AI-assisted or AI-enabled approach creates real leverage and when a straightforward deterministic solution is more appropriate.
• Communication: Ability to explain technical decisions, risks, blockers, and tradeoffs clearly to engineering and cross-functional partners.
• Continuous learning: Curiosity about rapidly changing models, tools, and development practices, paired with an evidence-based approach to evaluating them. You share what works, identify what does not, and help the team improve its practices over time.

We’ll be even more excited if you have:

• Experience building production AI-enabled features using model APIs, structured outputs, tool or function calling, retrieval, or agent workflows.
• Experience evaluating and operating AI-enabled systems through regression tests, evaluation harnesses, tracing, feedback loops, model or prompt versioning, cost and latency monitoring, or human-review paths.
• Experience extending AI development environments through MCP servers, agent tools, repository instructions, reusable skills or plugins, custom workflows, or automated code review.
• Production experience with Go or Python.
• Experience with gRPC, Protocol Buffers, generated clients, or service-oriented architectures.
• Experience with cloud infrastructure, containers, orchestration, or CI/CD.
• Experience with caching, background jobs, queues, or asynchronous processing.
• Experience with CMS, publishing, media, subscription, search, or other consumer-facing products.
• Experience operating production systems with observability, performance optimization or benchmarking, or accessibility testing.