Software Engineer 3

MongoDB•Quebec, ON

3d•CA$108,000 - CA$149,000•Remote

About The Position

The Agent Research and Tooling team, part of MongoDB's AI Builder Experience organization, owns the platform layer around agents: how teams author, distribute, evaluate, monitor, and improve agent skills and agent behavior. We are hiring a software engineer to build and maintain the tooling, evaluation systems, and quality gates behind MongoDB's agent skills. This is a software engineering role at the intersection of developer tooling, applied AI, and software quality. You will take loosely defined agent and tooling problems, break them into workable plans, and ship durable internal systems: command-line tools, reusable libraries, evaluation harnesses, and CI workflows. This role is open to remote work in Canada or can be based out of any of our Canada offices.

Requirements

2+ years of experience building production software, developer tools, internal platforms, or automation systems
Software engineering fundamentals in API design, testing, error handling, and maintainability
Experience building CLIs, libraries, test infrastructure, static analysis, or CI/CD workflows
Ability to design systems that are usable by developers and reliable in automation
Experience reasoning about correctness and safety with ambiguous input, nondeterministic output, false positives, or untrusted content
Comfort in an evolving R&D environment where the right abstraction emerges through prototypes and feedback
Written and verbal communication, including explaining technical trade-offs and aligning stakeholders across teams

Nice To Haves

Experience with agentic systems, LLM applications, prompt or rubric-based evaluation, or AI-assisted development
Experience building eval harnesses, benchmark datasets, quality metrics, LLM-as-judge workflows, or human-review tooling
Experience with Go, Python, JavaScript/TypeScript, Java, or C#
Experience with GitHub Actions security, secret handling, static rule engines, or policy enforcement
Experience moving prototypes into production

Responsibilities

Build and maintain agent skills and the infrastructure to validate, evaluate, publish, and maintain them
Design evaluation datasets and workflows that compare agent behavior against a baseline and produce actionable quality signals
Build agent metrics and observability: skill selection and routing, success and failure outcomes, tool calls, latency, and token usage
Design safety and quality gates for agent-authored content: rule packs, static analysis, confidence thresholds, structured verdicts, and bounded suppression
Create CLIs, libraries, and MCP integrations that other repositories adopt and that run in local development and CI
Integrate tooling into GitHub Actions and other CI workflows, including secrets, annotations, exit codes, and artifacts
Build code-generation quality checks, such as anti-pattern catalogs and linting for AI-generated MongoDB code
Investigate real failures such as nondeterministic results, false positives, and unsafe generated guidance, and turn them into reusable improvements
Collaborate with engineers, security partners, and product teams; communicate trade-offs, risks, and ownership across teams