Staff Software Engineer (Product)

Sharpist; Berlin; Hybrid; EUR 110.000 - 160.000; Vollzeit

Benefits dieser Stelle

Flexible working hours

Hybrid working model

Employee library

Company pension scheme

Coach with unlimited sessions

Free mate, beers & muesli

Zusammenfassung

Sharpist is seeking a Staff Product Engineer to shape AI-powered learning. You own features end-to-end, craft great UX, and build what helps people grow.

You’ll translate fuzzy problems into technically sound specifications, write the plan, implement, release, and measure impact with the team across TypeScript, React, Node.js, and cloud stacks.

Qualifikationen

• 10+ years of professional software engineering experience.
• Experience building AI or LLM product features, including prompting or guardrails.
• Experience in a B2B SaaS or HR tech environment.

Aufgaben

• Own multiple features end-to-end from concept to release.
• Write technical specifications, collaborate with engineers, and ship with impact.
• Use LLMs as core workflow tools for specifications, prototyping, and implementation.
• Talk to users and stakeholders to ground problems in real insight.

Kenntnisse

UX intuition

Ownership

English fluency

Prompt engineering

LLM tooling

Tools

TypeScript

React

React Native

Node.js

MongoDB

Redis

Docker

Google Cloud

BigQuery

Google Dataform

Lightdash

Prometheus

Grafana

Jobbeschreibung

• 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.

Benefits

• Flexible working hours
• Have your own certified coach with unlimited sessions
• Hybrid working model
• Employee library
• Company pension scheme
• Free mate, beers & muesli

You have strong intuition for UX: you notice what confuses users, what creates friction, and what feels rightYou can write a clear, technically grounded specification, and you know what makes one badYou think like a Product Engineer: you’ve owned full feature development, including defining the solution rather than only building what someone else specifiedYou 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 evidenceYou speak and write fluent EnglishYou can spot what LLMs miss: a wrong abstraction, a brittle data model, or a specification that looks fine until it meets production10+ years of professional software engineering experienceJudgment: You know when something is technically sound vs. technically plausible-but-painfulOwnership: When ownership is unclear, you step forward. When you’re blocked, you find a solution; you don’t shift the problem upClarity: You write specs that don’t need a meeting to explainSpeed: You move fast without creating rework for othersMission belief: You genuinely care about helping people. That’s why you’re hereExperience building AI or LLM product features, including prompting, evaluation, or guardrailsExperience working in a B2B SaaS or HR tech environment