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 backend tool work, or already comfortable with both
Can work across the full stack of an AI product, from the system prompt to the streaming frontend
Has a track record of mentoring engineers and raising the technical bar of their team
If this role interests you, we invite you to explore our public demo at demo.mercury.com.
Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.
Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.
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- Total Rewards
- The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits.
Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.
Our target new hire base salary ranges for this role are the following:
US employees (any location)
$200,700—$250,900 USD
Canadian employees (any location)
$189,700—$237,100 CAD
