Are you an SDE II who thrives on end-to-end ownership, gets energized by problems where correctness actually matters, and wants to work on systems that are actively being reimagined and not just maintained?
This role might be for you.
On Fee Tech, you will own systems that 2+MM sellers depend on daily to understand, predict, and act on their economics.
The work is genuinely end-to-end: you design, build, ship, and operate.
Our systems process ~68.5B transactions and used across 23 countries.
The engineering model is changing fast: we are rebuilding how fee policy is expressed, deployed, and validated using AI agents, formal verification (SMT), and self-improving RL loops.
You will work alongside senior and principal engineers, product managers, applied scientists, and ML engineers across the full fee lifecycle — from the Fee Calculation Engine through ML-based item classification to seller-facing AI surfaces and the agentic infrastructure beneath them.
If you are the kind of engineer who wants to see the direct impact of what you ship, you will see it here.
Key job responsibilities
- Design and implement software solutions for major features and improvements, balancing technical constraints with customer needs while ensuring scalability, performance, and security.
- Contribute across the full software development lifecycle including product definition, system design, coding, testing, deployment, and operational support, delivering incrementally and with high quality.
- Identify root causes of operational issues and implement permanent fixes, continuously improving your team's systems and technical processes to reduce support costs and increase reliability.
- Mentor and coach fellow engineers through code reviews and knowledge sharing, actively training new team members on system architecture, operations, and design decisions.
- Evaluate new technologies and approaches to solve problems using optimal data structures and algorithms, making thoughtful recommendations for adoption while keeping solutions as simple as possible.
- Operate AI-native engineering practices: use AI-assisted coding, multi-agent workflows, and agentic code review tools as your default mode of software delivery. A day in the life
You start your morning reviewing a pull request from a teammate, offering feedback that strengthens both the code and the engineer behind it.
After a quick standup, you dive into designing a new feature, sketching out how it fits into the broader system architecture.
In the afternoon, you might pair with a colleague to debug a tricky production issue, then document your findings so the team can learn from it.
Throughout it all, you collaborate closely with product partners to ensure what you build truly serves the customer.