Principal Machine Learning Engineer (f/m/x)

United States Digital Space LLC; Hamburg; Vor Ort; EUR 110.000 - 160.000; Vollzeit

Benefits dieser Stelle

Transport subsidy

Learning budget

Wellness and gym

Workation

Bi-weekly team lunches

Zusammenfassung

United States Digital Space LLC in Hamburg seeks a Principal Machine Learning Engineer to design, deploy, and scale production ML systems that directly drive product performance and revenue. You will own end-to-end ML lifecycle and collaborate across data science, data engineering, and product teams.

The role emphasizes turning insights into systems, leveraging AI tools daily, and working in a fast-paced, high-velocity environment with a hybrid work model in Hamburg.

Qualifikationen

• 7+ years of solid experience in machine learning, applied statistics, or data science.
• Proficient in Python and ML libraries (TensorFlow or PyTorch).
• Experience deploying models in production and maintaining scalable ML systems.

Aufgaben

• Design, build, and deploy ML models that impact product performance and revenue.
• Own end-to-end ML lifecycle from experimentation to deployment.
• Collaborate with Data Scientists, Data Engineers, and Product to translate insights into scalable ML solutions.

Kenntnisse

Python

TensorFlow/PyTorch

LLM APIs

Data Pipelines

SQL/Data Warehouses

Production Deployment

Communication

Tools

Databricks

Claude

Jobbeschreibung

Your Mission

This is a company built for growth. When you join the company, you’re stepping into a high‑velocity environment where data is not just analyzed but turned into systems that directly drive business performance. You will operate at the intersection of data, engineering, and product, solving real‑world problems in a space that demands both speed and precision.

We build high‑performing consumer products in one of the most competitive industries: mobile gaming. As part of a company on a path of hyper‑growth, you will help shape how machine learning becomes a core driver of our innovation, from smarter decision‑making to scalable, production‑ready intelligence across our platform.

If you’re looking for a role where you only experiment or stay in research, this isn’t it.

As a Principal Machine Learning Engineer (f/m/x) at the company, you’ll lay the foundation for how ML is built, deployed, and scaled across the company, turning insights into systems and models into measurable impact.

You’ll own the full ML lifecycle end to end, from first idea through production and continuous improvement, working closely with Data Science, Data Engineering, and Product. AI is core to how we operate, not just what we build: we use AI tools daily to work smarter, cut out repetitive work, and spend our time where it actually moves the needle. If that mindset speaks to you, you’ll fit right in.

What you’ll own

• Machine learning systems & impact – design, build, and deploy models that solve real business problems, focusing on production‑ready systems that directly influence product performance, user behavior, and revenue.
• End‑to‑end ML lifecycle – take ownership from experimentation to deployment and beyond, validating models, shipping them into production, monitoring performance, and continually iterating to improve outcomes in dynamic environments.
• Collaboration across data & engineering – work closely with Data Scientists and Analysts to translate insights, metrics, and hypotheses into scalable ML solutions, partnering with Data Engineers to ensure robust pipelines, feature availability, and reliable deployment.
• ML infrastructure & foundations – help define how ML is done at the company, establishing best practices, tooling, and architecture for model development, deployment, versioning, and monitoring, building the foundation for future scale.
• Translating ambiguity into systems – take loosely defined problems and turn them into structured ML solutions, making trade‑offs, defining approaches, and bringing clarity where none exists yet.

What you bring

• Strong applied ML experience – 7+ years of solid experience in machine learning, applied statistics, or data science, with a clear focus on real‑world, production use cases.
• Technical depth & programming skills – highly proficient in Python and experienced with modern ML libraries such as TensorFlow or PyTorch, writing clean, maintainable, and production‑ready code. Actively use AI tools in particular Claude and leverage LLM APIs to accelerate prototyping, research, and feature development.
• Data & infrastructure understanding – comfortable working with data pipelines and infrastructure, including SQL, data warehouses, ETL/ELT workflows, and Databricks.
• Production mindset – hands‑on experience with deploying, versioning, and monitoring models in real‑world environments, thinking beyond training toward scalability, reliability, and long‑term performance.
• Communication & translation skills – translate business and analytical questions into ML problems and clearly explain ML concepts to non‑technical stakeholders.
• Builder mentality – thrive in ambiguity and fast‑moving environments, creating structure where none exists, making pragmatic decisions, and taking ownership of outcomes from start to finish.

What you can expect

• Ambitious people, real ownership – work with driven, entrepreneurial people who care deeply about what they deliver, challenge each other openly, and own outcomes together.
• Top‑tier competition – our apps rank in the top 5 of their categories; we’re determined to push further, compete harder, and earn the number one spot.
• Growth focus – learning is built into the work through responsibility, feedback, and real challenges.
• Perks that help you focus – transport subsidy, learning budget, wellness and gym, workation, bi‑weekly team lunches, and more. The perks are great, but the real reason people join and stay is the challenge, the people, and the drive to win together.

Please note: this is not a remote‑only position; we offer a flexible hybrid model in Hamburg, Germany – working from home on Mondays & Fridays, coming to the office on Tuesday, Wednesday, and Thursday.