Product Development Engineer ; AI-Native; Onsite

Product Development Engineer I (AI-Native)

Our purpose is to help a billion people find the right work! Phenom is an AI-Powered talent experience platform that is redefining the HR tech space. We have grown into a global organization with offices in 6 countries and over 1,500 employees. As an HR tech unicorn organization, innovation and creativity is within our DNA. Come help us make every talent moment Phenomenal!

We are looking for a Product Development Engineer (AI-Native) who takes ideas and customer needs and turns them into working, validated product features and agentic capabilities — owning them from first prototype to production. Here, everyone wears multiple hats: the same person who builds a feature also ships it, supports the people using it, and debugs issues directly. You own outcomes end-to-end, not a narrow slice.

• Build innovative products on the Phenom platform — prototype fast, then harden what proves valuable into something customers can rely on.
• Take ideas to production — design, build, and validate features end-to-end, then keep improving them after they ship.
• Build and improve reusable agentic skills — packaged, versioned capabilities anyone in engineering can compose and re-run, so the next build starts from a proven asset, not a blank page.
• Practice AI-assisted, increasingly agentic engineering — drive PRs with coding agents and your own custom skills, and wire each PR to also update the observability it depends on.
• Run a controlled debugging loop — work from logs, traces, and prior incidents to find and fix root causes quickly.
• Own delivery to customers — deployment gates and hypercare, partnering with reliability engineers on deep platform issues.
• Keep rich context for agents — maintain the structured, per-customer context that every agent and skill draws on before it acts.
• Respect the guardrails — analysis is separated from action, high-risk changes require human approval, and every decision is logged for audit.

• 1-4 years of software development experience.
• Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related technical field required;
  Master's degree preferred.
• Strong fundamentals and critical thinking — AI raises the bar on judgment, it doesn't replace it.
• Comfortable across the stack and building with modern AI coding tools and agents.
• Customer empathy — you can sit with a customer, ask the right questions, and decide which problems are worth solving.
• End-to-end ownership — you finish what you start and keep improving it.
• Adaptable — eager to adopt new tools and reinvent your workflow as the tooling changes (often weekly).
• A strong communicator who aligns quickly with colleagues and customers.
• You thrive on variety — building, debugging, shipping, and customer calls in the same week.
• Bonus: experience building AI / agent products, workflow orchestration, observability, or building agent workflows on an Agent SDK.

• Programming proficiency — write clean, maintainable code in at least one modern language (e.g., Python, Java, JavaScript / Type Script, or Go).
• Computer science foundations — strong understanding of data structures, algorithms, complexity (Big-O), and decomposing problems into clean, testable components.
• Software design principles — object-oriented and functional concepts, clean-code practices, and sensible code and API design.
• Version control & collaboration — day-to-day fluency with Git, branches, pull requests, and code review.
• APIs & databases — working knowledge of REST APIs and JSON, plus basic SQL and data modeling with relational and/or No
  
  SQL stores.
• Testing & debugging — writing unit and integration tests and diagnosing issues methodically from logs, traces, and stack traces.
• Command line & cloud (a plus) — comfort in a Unix / Linux shell, with exposure to a cloud platform (AWS, Azure, or GCP) and CI/CD concepts.
• AI tooling (a plus) — hands-on with modern AI coding assistants and familiarity with LLM or agent concepts.

An AI-native engineering culture

• Big ownership, few meetings — we keep things small and autonomous, trust people to make local decisions, and protect focus…