Staff Software Engineer, Sharpist

• 10+ years of professional software engineering experience
• You think like a Product Engineer: you’ve owned full feature development, including defining the solution rather than only building what someone else specified
• You can write a clear, technically grounded specification, and you know what makes one bad
• You have strong intuition for UX: you notice what confuses users, what creates friction, and what feels right
• You use AI and LLM tools as a core part of how you work across specifications, prototyping, and implementation, not as a gimmick. Self-directed experiments and side projects count as evidence
• You can spot what LLMs miss: a wrong abstraction, a brittle data model, or a specification that looks fine until it meets production
• You speak and write fluent English
• Ownership: When ownership is unclear, you step forward. When you're blocked, you find a solution; you don't shift the problem up
• Clarity: You write specs that don't need a meeting to explain
• Judgment: You know when something is technically sound vs. technically plausible-but-painful
• Speed: You move fast without creating rework for others
• Mission belief: You genuinely care about helping people. That's why you're here

Desirable

• Join Sharpist as a Staff Product Engineer to shape AI-powered learning. Own features end-to-end, craft great UX, and build what helps people grow
• As a Staff Product Engineer, you sit where that product meets the people using it. Your work turns that support into something people actually use to reflect, learn, and act
• You get a business problem and own it from there: shaping the solution, writing the technical specification, working through implementation, releasing it, and checking whether it actually worked
• There is no hand-off in the middle and no shipping blind
• We’re working toward a one-week cycle, although we’re not there yet
• For now, we’d rather ship at roughly 70%, learn from real usage, and improve the next version than spend too long polishing something in private
• The difficult part isn’t coding on its own
• It’s the synthesis: taking a fuzzy problem and turning it into a technically sound, properly scoped solution quickly enough to keep the cycle moving
• That’s where many engineers slow down
• This role is here to close that gap
• LLMs now handle a growing share of implementation, and you should use them as part of your everyday workflow
• What they can’t replace is the judgment to recognise when the architecture is wrong, when an abstraction won’t hold, or when a shortcut is likely to become next quarter’s incident
• The important thing is catching that before it gets built, rather than after it reaches production
• First 30 days:
• Get deep into the Sharpist product: the AI Coach, the coaching platform, and the learner journey
• Audit existing solution concepts and technical specifications so you understand what’s shipping and why
• Shadow a full problem-to-delivery cycle with the engineering team
• Ship your first improvements to the product
• First quarter:
• Own your first problem end-to-end: define the problem space, design the solution, and write the technical specification
• Use LLMs as a core workflow tool: prompt for specifications, evaluate the results for efficiency and soundness, and iterate
• Work directly with engineers to make sure what gets built matches what was intended
• Talk directly to users and stakeholders, grounding each problem in real insight rather than assumptions
• Give structured feedback on technical specifications from others, especially around technical feasibility, scope, and edge cases
• Year one:
• Own multiple features end-to-end: define them, ship them, measure them, and understand what worked and what didn’t
• Contribute to the team’s weekly give & take by sharing what you learned and picking up what others discovered
• Make the developer experience meaningfully better through tooling, workflow, or process improvements the team actually uses
• The Stack: TypeScript, React, React Native, Node.js, MongoDB, Redis, Docker, Google Cloud, BigQuery, Google Dataform, Lightdash, Prometheus, Grafana.