At Sharpist, our mission is clear: to empower everyone to lead a self‑aware career and life.
We make coaching accessible for all – anytime, anywhere.
We’re building a hybrid coaching experience: genuine 1:1 coaching for empathy and accountability, combined with an AI Coach for quick insights, follow‑ups, and in‑the‑moment support.
Our goal: to make coaching scalable – not to replace humans, but to enhance them. Human and intelligent.
🎥 Meet our AI Coach in action — and see how Sharpist empowers leaders and talents to grow every day:
You get a business problem and you own it from there: solution concept, PRD, implementation, release, and measuring whether it worked. No hand-off, no shipping blind. We're working toward a one-week cycle, but we're not there yet. We'd rather ship at ~70% and learn from real usage than polish in private.
The hard part isn't the coding. It's the synthesis: going from a fuzzy problem to a technically sound, scoped solution fast enough to keep the cycle moving. That's where most engineers slow down. That's the gap this role fills.
LLMs handle a growing share of the implementation. What they can't replace is the judgment to know when the architecture is wrong, when an abstraction won't hold, when a shortcut becomes next quarter's incident. Catching that before it's built, not after.
- Get deep into the Sharpist product: the AI Coach, the coaching platform, the learner journey
- Audit existing solution concepts and PRDs; understand what's shipping and why
- Shadow one full problem-to-delivery cycle with the engineering team
- Ship your first small improvements or bug fixes
- Own your first problem end-to-end: define the problem space, design the solution, write the PRD
- Use LLMs as a core workflow tool: prompt for specs, evaluate for efficiency and soundness, iterate
- Work directly with engineers to ensure what gets built matches what was intended
- Talk directly to users and stakeholders to ground each problem in real insight, not assumptions
- Give structured feedback on PRDs from others: technical feasibility, scope, edge cases
- Own multiple features end-to-end: define, ship, measure, and know what worked and what didn't
- Contribute to the team's weekly give & take: share what you learned, pick up what others discovered
- Make the developer experience meaningfully better: tooling, workflow, or process improvements the team actually uses
TypeScript, React, React Native, Node.js, MongoDB, Redis, Docker, Google Cloud, BigQuery, Google Dataform, Lightdash, Prometheus, Grafana.
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