Responsibilities
- Take full ownership of complex technical challenges, from identification and reproduction to resolution, spanning APIs, integrations, client-side code, agent behavior, and data systems, stepping in directly when needed to accelerate fixes.
- Investigate and determine the root cause of agent errors or incorrect outputs, distinguishing between issues in prompts, knowledge bases, configurations, or models, and implement durable fixes that hold under real-world usage.
- Ensure issues do not recur by building tools, runbooks, or system-level safeguards; for every fix, evaluate how the team can handle similar cases in the future and what systemic improvements can prevent recurrence.
- Act as a structured, data-driven bridge between customer-facing issues and product development, identifying patterns in user problems and influencing roadmap decisions to improve overall product quality.
- Elevate team capability by sharing knowledge and tooling so others can resolve complex issues independently, reducing reliance on individual expertise.
- Clearly identify the boundaries of your responsibility and escalate genuine product defects, infrastructure failures, or outages with clear, reproducible examples rather than deferring action.
Benefits
- Competitive compensation with equity or stock grants included
- Dedicated annual budget for professional development and learning
- Premium access to leading AI tools including ChatGPT, Claude, Perplexity Pro, Cursor, and Lovable, plus a quarterly stipend for new software and utilities
Compensation
Great salary plus equity or stock grants
Work Arrangement
Remote (Worldwide)
What success looks like
The point of this role isn't closing tickets faster. It's that the product visibly gets better because you've killed the root cause of recurring problems. Resolution is the floor. Product quality is the win. - By 90 days: you've named the top recurring failure patterns and you're attacking them at the root, not patching symptoms. At least one class of problem is fixed in the product itself, not just resolved ticket by ticket, so it stops showing up at all. Resolution is handled without pulling engineers off the roadmap, but the real signal is fewer problems existing in the first place. - By 6 months: recurring issue classes are measurably down because the root causes are gone, the product is demonstrably more robust, and your feedback loop to Product is shipping real improvements. The team resolves more on its own because the system catches the next one, not heroics.
Why Siena?
- Real ownership. You will own problems, not tasks, with the autonomy to solve them your way and the trust to do it without a manager in your inbox. - Work that ships and matters. What you build reaches real consumer brands fast. - A category we are creating. The Agent of Record is new ground, with unsolved problems and cutting-edge models, not a crowded field to compete in. - A team that respects your judgment. Globally distributed, async-first, low bureaucracy. We hire adults and treat you like one. - Great salary plus equity or stock grants. Own a piece of what you build. - Learning budget. If you are growing, so are we. - AI-fluency by default. Few places will push your work with production AI further or faster.
Our values
The people who thrive here are curious, customer-obsessed, and take ownership without being asked. They fix problems first and explain later. They're direct about feedback—both giving and receiving it—because they care more about getting things right than being polite. They maintain high standards while moving at startup speed, and they build real relationships with teammates because they know that's how great work gets done. When things get tough, they adapt and keep pushing forward.
Our approach to AI
The people who thrive here treat AI like a natural extension of themselves. They've built their own ecosystem of agents - some for research, others for debugging, writing, analysis, or writing code. They know which AI works best for what problem. Everyone gets premium accounts (ChatGPT, Claude, Perplexity Pro, Cursor, Lovable) plus a quarterly budget for new tools. But the magic happens in how we share knowledge. Demo days where someone shows off a clever workflows. Slack threads about which model handles a specific use case better. Learning from each other's AI workflows.
