Software Engineer: Self Improvement

The job

You turn Bonsai’s compounding self improvement loop into a product that non-technical people can use. As someone works, Bonsai learns their context, procedures, judgment, and taste, and uses that to help with the next task. You own how real work becomes improvements, how we prove those improvements help, and how people see and control them without writing prompts or configuring an agent.

Problems you’ll work on

These are examples, not lanes. You will move between them, while taking real ownership of the ones that match your interests.

• Learning from use: Every session is evidence of how someone likes their work done. How do you spot a repeated workflow or a correction, turn it into an improvement, and propose it at a moment the person will say yes, without burying them in suggestions they didn’t ask for?
• The right improvement at the right time: One person may end up with hundreds of skills and memories. How does an agent pick the few that apply to this task, combine them, and ignore the rest, inside a finite context window?
• Proving an improvement helped: A skill or a memory that reads well can still make outcomes worse. How do you evaluate improvements against real tasks, notice when one goes stale or starts hurting, and fix or retire it?
• Improvement people can see and steer: Most people will never read a prompt or edit a config file. How do you show what Bonsai learned, let someone keep, change, or reject it, and keep them in control without asking them to manage anything?
• Improvements that travel: What Bonsai learns from one person’s work can help their team too. How do you share it without leaking private context, and keep it improving as others use it?

What you’ll do

• Build the self improvement loop end to end: capture evidence from real work, propose improvements, evaluate them, and make the ones that help available for future work.
• Build the skill runtime: discovery, retrieval, loading, and composition inside the agent loop.
• Build the pipeline that turns session history into new skills, new memories, and improvements to existing ones, and the review surfaces where a person accepts, edits, or rejects them.
• Design and build the experience for non-technical people: what Bonsai learned, why it suggests it, and how to review, change, or undo it.
• Build evals for improvements: task suites, graders, and production signals that tell us whether a change helped.
• Own sharing: team and marketplace distribution, versioning, and provenance.

Who you are

• You’ve built with LLMs past the prompt: retrieval, evals, agent loops. You know a prompt change is a code change, and you test it like one.
• You write very well. A skill is instructions for a model, and most bad skills are bad writing.
• You care about measurement. You can design an eval that tells you something true, and you distrust one that only ever goes up.
• You have good product instincts. You know when to interrupt someone and when to stay quiet, and you can make a complex system useful to someone who has never heard of a context window or an eval.
• You lean on skills, rules files, or custom instructions heavily in your own work, and you have opinions about what makes them work.

Bonus

• You’ve built recommendation, ranking, or retrieval systems in production.
• You’ve built LLM eval infrastructure.

About Yaklabs

Yaklabs is building a desktop AI application that turns knowledge workers into AI power users, without docs, API keys, or knowing what to ask for. One surface for agentic AI work: proactive, personalized, progressively powerful. The gap between AI power users and everyone else isn’t talent, it’s setup. We close it.

The team: Alex Goddijn spent four years at Palantir, then founded an agent harness company that was acquired by Poolside AI. Seb Goddijn led internal AI at Ramp. We’re building Bonsai so that kind of leverage isn’t limited to companies with their own internal AI team.

We have funding. Product and design work in person in New York City, and some engineering roles are remote. Small team, high intensity, no passengers.

The deal

• Early team. $215k to $325k base salary for US-based hires, plus meaningful equity.
• In person in New York City for product and design roles. Some engineering roles are remote, and each posting lists its location.
• You’ll shape the product, the culture, and the company. This is a founding-team role.

every role

Who we look for

• Technical builders with a domain edge. Everyone here is technically minded and can build and ship. You bring a distinctive strength in a domain: design, systems, product, infrastructure, or another area, and use it to take a problem from first principles to working software.
• First principles thinking. We’re building in a category that’s being defined in real time. Best practices don’t exist yet. You reason from the problem, not from what everyone else is doing.
• High agency and ownership. You don’t wait to be told what to do. You see what needs to happen, you go make it happen, and you own the outcome. At a team this size, there’s nobody to delegate to.
• Deep curiosity about AI tooling. Not just using ChatGPT or Copilot: you’re experimenting, writing your own tools, trying new agent frameworks the week they drop, forming opinions about what works and what doesn’t. The people building this product should be the most demanding users of the category. If you’ve shipped PRs to open-source agentic harness projects, tell us.
• You build compounding leverage. In the era of building with agents, one person doesn’t just ship one person’s work, they build the systems that build the product. Every person is amplified, and with that amplification comes the responsibility to build things that compound: skills, automations, agent workflows that make the next thing faster. This is a new way of working, and you’re excited to develop the skillset it demands.

How we hire

YakLabs is an equal opportunity employer. We consider every application without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other protected characteristic. If you need an accommodation at any point in the process, email careers@yaklabs.ai and we will work it out with you. A person, not a machine, reads every application.