Senior Software Engineer - Mercury Command

In 1965, an engineer in Scotland was given a mundane task with a tricky problem to it - banks wanted to close on Saturdays and still serve customers, but they didn’t know how to solve the authentication of the right user. James Goodfellow, working at Smiths Industries, uncovered the insights that you needed something you have (a card) and something you know (a PIN). Because someone told him they could only remember 4 digits instead of his proposed 6, today over 3 million ATMs use a card+4 digit PIN over 60 years later. 

Like the ATM, Mercury is building technology that pushes forward financial interfaces for the long term. Command is Mercury's LLM-powered financial assistant, launched to all customers in June 2026. It lets users understand their finances and take action in plain language, from asking about cash flow to sending payments, issuing cards, and managing invoices. With the product now in customers' hands, the work is to evolve it, extend its capabilities, and find new ways to leverage LLMs to give Mercury customers a more powerful banking* experience.

What you'll do

Ship new capabilities users love:

• Design and ship new Command skills, the domain-specific instruction sets that teach the model how to handle workflows like sending money, managing invoices, and understanding cash flow
• Design and build agentic workflows in Command, defining the architecture for how multi-step agent interactions should work as we extend what the product can do on a customer's behalf
• Work with backend teams to define tool schemas for new capabilities, shaping the data contracts between Mercury's business logic and the model
• Own new capabilities end to end, from the system prompt to the frontend component that renders the response

Own the LLM layer:

• Maintain and evolve Command's prompt architecture: the system prompt, skill loading system, session context, and the policy and compliance layers underneath
• Tune model behavior: reasoning effort, prompt caching strategy, fallback chains, and the streaming patterns that make the product feel fast
• Stay current with how models are evolving and bring that knowledge back to how Command is built

Build quality in:

• Write and expand Command's eval harness, adding cases that cover new capabilities and scoring rubrics that detect regressions before users do
• Partner with product and compliance teams to define what "working correctly" means for each new capability, then build the tests that prove it
• Own the reliability and quality of what you ship, from initial design through post-launch monitoring

This list is illustrative. Command is a product in motion and priorities will shift as we learn. The right person will help choose the next highest-leverage work.

The ideal candidate

• Has 7 or more years of software engineering experience, with deep technical expertise building and scaling LLM-powered applications in production
• Has gone beyond shipping a first version: you have scaled an LLM-powered product, dealt with the reliability and performance problems that come with real usage, and made it better over time
• Has experience designing agentic systems and has opinions about how to architect multi-step workflows that are reliable, explainable, and safe to run on behalf of real users
• Has built eval infrastructure and can write cases that actually measure whether the product works, not just whether the model outputs something plausible
• Understands the real tradeoffs in LLM deployments: latency, cost, compliance, and what breaks in production that doesn't show up in demos
• Has opinions about what makes an AI product trustworthy, not just impressive, and can build toward that bar
• Is comfortable with TypeScript and willing to learn Haskell for