Junior Full-Stack Engineer — Shape AI Tax Workflows

Filed; Stockholms kommun; On-site; SEK 670,000 - 781,000; Full time

Benefits offered by this job

30 days of holiday per year (Europe/US

Fully covered medical expenses in the,

European benefits package

Company-wide offsites every four weeks

Job summary

Filed is an AI tax preparer trusted by large accounting firms, based in Sweden. We are looking for a Junior Full Stack Engineer to build the product surface and the platform underneath it, spanning document intake, review, and delivery processes.

You'll ship across the stack, learn quickly from seasoned engineers, and work with demanding professional users. The role emphasizes fast delivery, strong collaboration, and ongoing learning about LLMs to improve accuracy and transparency.

Qualifications

• Working knowledge of TypeScript, React, and Python.
• Familiarity with cloud infrastructure on Google Cloud and/or Azure is a plus.
• Bonus if you have shipped anything real: a side project, an internship product, an open source contribution.

Responsibilities

• Building and maintaining fullstack features across our AI tax prep, planning, and review products.
• Owning the UX and backend logic for the workflows tax professionals use every day.
• Shipping fast, watching metrics, and deciding what to improve next, without being told.
• Learning about LLMs and making them work for our customers to achieve higher accuracy and transparency.
• Keeping the codebase clean, typed, and fast as we scale.

Skills

TypeScript

React

Python

Google Cloud

Azure

LLMs

Tools

Cloud infrastructure

OpenAI API

Job description

Filed is an AI tax preparer trusted by large accounting firms, based in Sweden. We are looking for a Junior Full Stack Engineer to build the product surface and the platform underneath it, spanning document intake, review, and delivery processes.

You'll ship across the stack, learn quickly from seasoned engineers, and work with demanding professional users. The role emphasizes fast delivery, strong collaboration, and ongoing learning about LLMs to improve accuracy and transparency.