The Opportunity

We're hiring a Senior Software Engineer to help build and scale our platform. This role is for engineers who work fluently alongside AI tools and agent frameworks — not as a novelty, but as a core part of how modern software gets built. You'll ship production systems, design architectures that scale, and raise the bar for code quality on a team where AI is a daily collaborator.

We don't want someone who just uses AI to type faster. We want someone who uses AI to think bigger — and who has the judgment to know when the AI is wrong, when it's right but unwise, and when to do the work themselves.

What you'll do

• Design, build, and operate production services that serve our customers — backend, infrastructure, and the integration points in between.
• Lead technical projects from ambiguity to shipped feature: scope, design, build, deploy, measure, iterate.
• Use AI coding tools (Claude Code, Copilot, Cursor, or whatever fits the task) as a force multiplier — to draft, refactor, explore, and review — while staying accountable for everything that ships under your name.
• Build and integrate AI-powered features into our product where they create real customer value: agent workflows, LLM-backed APIs, retrieval systems, and evaluation pipelines.
• Review code rigorously — including AI-generated code — and help establish standards for how the team incorporates AI output into the codebase safely.
• Debug hard problems in production. Trace through systems you didn't build. Form hypotheses, verify them, and fix the cause, not the symptom.
• Mentor other engineers, especially on the meta-skills that matter most now: judgment, taste, verification, and knowing when to push back on AI suggestions.
• Contribute to architectural decisions and longer-term technical strategy.

What we look for

Core engineering skills

• 7+ years of professional software engineering experience building and operating production systems.
• Strong fundamentals: data structures, concurrency, distributed systems, databases, networking, security. The kind of depth AI tools can accelerate but not replace.
• Production sense: you know what breaks at scale, how to add observability, how to roll out changes safely, and how to debug an incident.
• Code review judgment: you can read a PR and spot the subtle bugs, the security holes, the architectural smells — including the ones AI tends to generate.
• Clear technical writing: you can write a design doc that another engineer can build from, and you can explain a hard problem in plain language.

AI-native engineering practices

• Fluent use of AI coding tools. You use them daily and you use them well — knowing when to delegate, when to verify, and when to write something yourself.
• Healthy skepticism. You catch hallucinated APIs, subtly broken logic, and security mistakes in AI output. You don't paste-and-pray.
• Experience with LLM-backed systems. You've worked with at least one of: agent frameworks (LangGraph, AutoGen, custom), tool/function calling, RAG pipelines, prompt evaluation, or model fine-tuning.
• Understanding of failure modes. You understand the ways LLMs and agents fail in production: hallucination, drift, prompt injection, latency variance, cost blowups, and the operational practices that mitigate them.
• Evaluation mindset. You measure model and agent quality with real metrics, not vibes. You've built or used eval harnesses, regression suites, or human-in-the-loop review processes.

Judgment and collaboration

• Ownership - You take responsibility for the code you ship — whether you wrote it yourself or an agent drafted it. "The AI did it" is not an excuse you accept from yourself or others.
• Taste - You can tell good design from bad. You push back on over-engineering and you push back on tech debt that compounds.
• Communication - You translate fuzzy product requirements into concrete specs, ask the questions that surface hidden assumptions, and write things down.
• Mentorship - You make the engineers around you better, especially at the new skills the field demands.

Nice to have, not required

• Experience building or operating production agent systems (multi-step, tool-using, with feedback loops).
• Contributions to open-source AI tooling or agent frameworks.
• Experience in EdTech domain.
• Track record of mentoring or technical leadership beyond your own work.