Software Engineer 3
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.
What you'll do
- 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
Examples of the problems you'll solve
- How can tests verify an agent tool's
structured result when item order may vary, but counts, required fields, and values must remain correct
- How 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 instruction
- How can an evaluation suite show whether a skill improves answers over a baseline and give
authors enough signal to improve it
What we're looking for
- 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 have
- 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
What success looks like
In your first year, you will:
- Ship tooling that makes agent
skills or developer workflows easier to test, review, and adopt
- Improve the quality and interpretability of evaluations,
not just their count
- Convert recurring manual work and fragile scripts into documented, reusable automation
- Make
security, correctness, and operational trade-offs explicit in the designs you ship
- Earn adoption from partner teams
through clear interfaces and reliable CI
- Own projects independently while collaborating on shared systems
About MongoDB
MongoDB 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, 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, 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, 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: 2273504602
AI 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