Staff Product Engineer

Introduction

We are hiring Product Engineer to take full ownership of feature development, bridging the gap between fuzzy business problems and technically sound solutions. You will join us in our mission to make coaching scalable by building a hybrid experience that combines human empathy with intelligent AI support.

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.

Your tasks

Activities

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.

What you’ll do

First 30 days:

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

First quarter:

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

Year one:

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

The Stack

TypeScript, React, React Native, Node.js, MongoDB, Redis, Docker, Google Cloud, BigQuery, Google Dataform, Lightdash, Prometheus, Grafana.

Your profile

Requirements

Must-haves:

You think like a Product Engineer: you've had full ownership of feature development, defining the solution, not just building what someone else specified

You can write a clear, technically grounded PRD, and you know what makes one bad

You have a strong intuition for UX: what confuses users, what creates friction, what feels right

You use AI/LLM tools as a core part of how you work, across specs, 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, a spec that looks fine until it hits production

Fluent English (written and spoken)

Nice to have:

Experience building AI/LLM product features (prompting, evaluation, guardrails)

Worked in a B2B SaaS or HR tech environment

Team

We offer

You will join an engineering team that values a 'builder' culture, focusing on shipping, learning, and outcomes. We prioritize in-person collaboration to build strong relationships and foster the kind of conversations that evolve our product. The Engineering team consists of 5 people.