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.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 work from home in Canada or can be based out of any of our Canada offices.What you'll doBuild and maintain agent skills and the infrastructure to validate, evaluate, publish, and maintain themDesign evaluation datasets and workflows that compare agent behavior against a baseline and produce actionable quality signalsBuild agent metrics and observability: skill selection and routing, success and failure outcomes, tool calls, latency, and token usageDesign safety and quality gates for agent-authored content: rule packs, static analysis, confidence thresholds, structured verdicts, and bounded suppressionCreate CLIs, libraries, and MCP integrations that other repositories adopt and that run in local development and CIIntegrate tooling into GitHub Actions and other CI workflows, including secrets, annotations, exit codes, and artifactsBuild code-generation quality checks, such as anti-pattern catalogs and linting for AI-generated MongoDB codeInvestigate real failures such as nondeterministic results, false positives, and unsafe generated guidance, and turn them into reusable improvementsCollaborate with engineers, security partners, and product teams; communicate trade-offs,risks, and ownership across teamsExamples of the problems you'll solveHow can tests verify an agent tool's structured result when item order may vary, but counts, required fields, and values must remain correctHow can a CI gate flag unsafe instructions in an agent skill without treating every neutral mention as an incident or letting cautionary wording hide a real instructionHow can an evaluation suite show whether a skill improves answers over a baseline and give authors enough signal to improve itWhat we're looking for2+ years of experience building production software, developer tools, internal platforms, or automation systemsSoftware engineering fundamentals in API design, testing, error handling,



and maintainabilityExperience building CLIs, libraries, test infrastructure, static analysis, or CI/CD workflowsAbility to design systems that are usable by developers and reliable in automationExperience reasoning about correctness and safety with ambiguous input, nondeterministic output, false positives, or untrusted contentComfort in an evolving R&D environment where the right abstraction emerges through prototypes and feedbackWritten and verbal communication, including explaining technical trade-offs and aligning stakeholders across teamsNice to haveExperience with agentic systems, LLM applications, prompt or rubric-based evaluation, or AI-assisted developmentExperience building eval harnesses, benchmark datasets, quality metrics, LLM-as-judge workflows, or human-review toolingExperience with Go, Python, JavaScript/TypeScript, Java, or C#Experience with GitHub Actions security, secret handling, static rule engines, or policy enforcementExperience moving prototypes into productionWhat success looks likeIn your first year, you will:Ship tooling that makes agent skills or developer workflows easier to test, review, and adoptImprove the quality and interpretability of evaluations, not just their countConvert recurring manual work and fragile scripts into documented, reusable automationMake security, correctness, and operational trade-offs explicit in the designs you shipEarn adoption from partner teams through clear interfaces and reliable CIOwn projects independently while collaborating on shared systemsAbout MongoDBMongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the data platform for the AI era, enabling builders to create, transform, and disrupt industries with software. MongoDB's unified data platform, the most widely available, globally distributed data platform on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed,



multi-cloud data platform and is available across AWS, Google Cloud, and Microsoft Azure.With offices worldwide and over 67,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we're powering the next era of software.Our compass at MongoDB is our Leadership Commitment, (https://www.Mongodb.Com/company) guiding how and why we make decisions, show up for each other, and win. It's what makes us MongoDB.To drive the personal growth and business impact of our employees, we're committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy (https://www.Mongodb.Com/company/blog/culture/employee-benefits-that-make-a-difference-at-mongodb), we value our employees' wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it's like to work at MongoDB (https://www.Mongodb.Com/blog/channel/culture), and help us make an impact on the world!MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.MongoDB is an equal opportunities employer.Red ID: 2273504602AI is used to review applications based on job-related criteria and does not replace human decision-making. The hiring team decide who moves forward.MongoDB's base salary range for this role is posted below. Compensation at the time of offer is unique to each candidate and based on a variety of factors such as skill set, experience, qualifications, and work location. Salary is one part of MongoDB's total compensation and benefits package. Other benefits for eligible employees may include: equity, participation in the employee stock purchase program, flexible paid time off, 20 weeks fully-paid gender-neutral parental leave, fertility and adoption assistance, Registered Retirement Savings Plan (RRSP) with employer match, mental health counseling, backup child and elder care, and health, dental, and vision benefits offerings. Please note, the base salary range listed below and the benefits in this paragraph are only applicable to candidates based in Canada.MongoDB's base salary range for this role in Canada is:$108,000—$149,000 CAD #J-18808-Ljbffr

📌 Software Engineer 3 (Edmonton)
🏢 MongoDB
📍 Edmonton