Staff Software Engineer, Product (m/w/d)

Sharpist; Berlin; Hybrid; EUR 120.000 - 160.000; Vollzeit

Zusammenfassung

Sharpist is seeking a Staff Product Engineer to bridge product and users, turning insights into shipped solutions using AI-assisted coaching features.

You will own end-to-end initiatives from problem definition to delivery, with a focus on scalable architectures, LLM-enabled tooling, and a fast feedback loop in a hybrid Berlin setting.

Join a team that values fast learning, strong engineering judgments, and cross-functional collaboration.

Aufgaben

• Own end-to-end features from problem to delivery.

Tools

TypeScript

React

React Native

Node.js

MongoDB

Redis

Docker

Google Cloud

BigQuery

Google Dataform

Lightdash

Prometheus

Grafana

Jobbeschreibung

About Sharpist

Sharpist exists to help people become more self-aware and effective at work and in life.

We combine human coaching with an AI Coach so that employees at every level can access meaningful support when they need it.

Our goal: to make coaching scalable - not to replace humans, but to enhance them.

The Role

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.

Meet our AI Coach in action - and see how Sharpist empowers leaders and talents to grow every day:

The Role

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.

What you'll do

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.

Working Model

Hybrid in Berlin, 3 days per week in the office. We find that being together in person is what builds the kind of relationships where real conversations happen: the ones that change how you think, how you work, and how the product evolves.