Senior Software Engineer, Agentic Harness Quality and Performance
A senior IC at Goodnotes' AI-native Paddington team owns the quality practice for coding-agent-driven development: building the agentic harness, static analysis, pre-commit/CI checks, and risk gates across iOS, Android, Mac, Windows, web, and backend, while teaching squads to adopt and extend it. On-site in London, permanent, no visa sponsorship.
- Compensation
- Not disclosed
- Location
- London
- Work setup
- Not specified
- Seniority
- Senior
- Employment
- Full time
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- 5+ years in software or quality engineering, including quality frameworks, platforms, or harnesses for a team that ships through coding agents
- Builds agentic workflows yourself, beyond chat
- Static analysis in depth: custom lint rules, dependency rules, and messages that guide a fix
- Designs pre-commit and CI flows, with fast checks early and slow checks after integration
- Codes in at least one of Swift, TypeScript, Rust, Go, or Java
- Writes scripts and small systems fast
- Makes teams self-serve; others adopt and extend your tools
- Judges "good enough"; keeps rules and feedback from overwhelming the team or the agents
- Works with engineers, designers, and product; sets own agenda, acts before asked, handles ambiguity and conflict
- Works on-site 5 days per week in the London Paddington office
Nice to have
- Built LLM-as-judge checks or eval suites for AI product output
- Wrote architecture fitness functions for performance, security, or observability
- Knows web, desktop, or mobile test tools, iOS and Android in particular
- Worked on a full-cycle team and operated what you built
Full description
Good Systems is the parent company behind Goodnotes, where millions of people capture and develop their ideas.
Goodnotes is where we started, and it’s now the foundation for a growing family of products. By reimagining how we interact with information, we’re bringing together human creativity and the breakthrough capabilities of AI.
We build products that help people turn good thinking into good work.
Our Values
Dream big
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Be visionary, strategic, and open to innovation
Build great things
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Work in service of our users, always improving and pushing higher
Operate like an owner
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Take responsibility with bold decision-making and bias for action
Win like a sports team
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Be trusting and collaborative while empowering others
Learn and grow fast
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Never stop learning and iterate fast
Share our passion
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Share ideas and practice enthusiasm and joy
Be user obsessed
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Empathetic, inquisitive, practical
About the role
You will join Paddington, our AI-native product line inside Goodnotes, and work directly with Steven, our founder, and a small team shipping new products. Engineers own the full cycle: they design, build, test, ship, and operate what they ship. Coding agents write the product code, and the engineer who merges it owns what lands.
You will ship product changes with a squad and build the harness around those agents: guides and automated checks that catch defects and performance regressions before the code merges. When a defect reaches users, or a user-facing path gets slower, you change the product code or the check and show the measurement. You'll be an individual contributor reporting to the Engineering Manager. We hire at Senior level and welcome Staff-level candidates.
- This role will be required to work on-site, 5 days per week in our London Paddington office
- This is a permanent role
- This role does not include visa sponsorship
This is the role for you if you're excited to work on:
- You start in a product squad on day one and ship code users can run. You keep 20 to 30 percents of your time in a squad after that, and the checks you write come from that release path.
- You design the guides and checks that steer the coding agent before it edits and correct the edit after. Fast, deterministic checks run in the agent session and at pre-commit. Slower checks run in CI. Every message tells the agent how to fix the finding. You track which checks fire and remove the ones that never do.