Senior Software Engineer | Collaboration & Asset Management at DeepL
Köln, Nordrhein-Westfalen, Germany -
Full Time


Start Date

Immediate

Expiry Date

25 Jun, 25

Salary

0.0

Posted On

26 Mar, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

MEET DEEPL

DeepL is a global communications platform powered by Language AI. Since 2017, we’ve been on a mission to break down language barriers. Our human-sounding translations and intelligent writing suggestions are designed with enterprise security in mind. Today, they enable over 100,000 businesses to transform communications, reach new markets, and improve productivity. And, empower millions of individuals worldwide to make sense of the world and express their ideas.
Our goal is to become the global leader in Language AI, building products that drive better communication, foster connections, and make a real-life impact. To achieve this, we need talented individuals like you to join our exciting journey. If you’re ready to work with a dynamic team and build your career in the fast-moving AI space, DeepL is your next destination.

Responsibilities
  • Design, build, and maintain features within a microservices-driven architecture in a hybrid cloud environment, from early design phases to production deployment, collaborating with multiple engineering teams.
  • Enhance software maintainability, reliability, and performance through robust coding standards, automated testing, and continuous improvement.
  • Foster a collaborative engineering culture through pair programming, mob programming, and knowledge-sharing activities—coaching others and championing best practices.
  • Maintain a documentation-first mindset, promoting clarity and efficient onboarding across the organization.
  • Work closely with product managers and designers to clarify feature scope, prioritize user needs, and align solutions with product strategy.
  • Leverage data-informed approaches—metrics, observability, usage trends—to guide engineering decisions and validate outcomes.
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