Machine Learning Engineer, Applied AI arbeitnow Bjak Germany · 10/16/2026

Primetime; Deutschland; Remote; EUR 90,000 - 130,000; Full time

Job summary

Primetime is seeking an ML engineer to build and improve ML systems powering our AI-native applications. You will own model development, production deployment, and evaluation across classification, ranking, retrieval and personalization.

You'll develop efficient training, inference and deployment pipelines using Python, PyTorch and JAX, work with embeddings and vector search, and balance accuracy, latency and cost while upholding privacy.

Qualifications

• Strong foundations in machine learning and modern neural architectures.
• Hands-on experience building and deploying ML models in production.
• Experience in classification, ranking, retrieval, recommendation systems or personalization.
• Understanding of model evaluation, experimentation and performance metrics.
• Able to balance model quality, latency, cost and reliability.

Responsibilities

• Build and improve ML systems powering our AI-native applications.
• Develop and deploy models for classification, ranking, retrieval, and personalization.
• Own end-to-end ML pipelines from data to production deployment.
• Monitor model performance and shipping improvements based on real-world feedback.

Skills

Python

PyTorch

JAX

Embeddings

Vector search

Model deployment

Performance optimization

Job description

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.

Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.

Role

Build and improve ML systems that power our AI-native applications.

Work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment.

This role is for engineers who enjoy solving practical ML problems, improving model quality and shipping reliable systems used by real users.

Focus

Build and improve ML models for classification, ranking, retrieval and personalization.

Develop efficient classifiers and optimize model accuracy, precision, latency and cost.

Improve search relevance and context retrieval using embeddings, hybrid search and reranking.

Build personalization models that learn from user behavior while respecting privacy.

Develop evaluation pipelines, labeled datasets and metrics to measure model quality.

Fine-tune and adapt existing models when supported by sufficient data.

Build and maintain ML training, inference and deployment pipelines.

Debug production issues, monitor model performance and ship improvements based on real-world feedback.

Python

PyTorch / JAX

Embeddings, vector search and ranking models

Production ML systems and inference pipelines

Ideal Experience

Strong foundations in machine learning and modern neural architectures.

Hands-on experience building and deploying ML models in production.

Experience in classification, ranking, retrieval, recommendation systems or personalization.

Understanding of model evaluation, experimentation and performance metrics.

Comfortable writing production-quality code and working with real-world datasets.

Able to balance model quality, latency, cost and reliability.

Strong ownership, problem-solving skills

Outcomes

ML models in production meet expected accuracy, relevance, latency and reliability targets.

Classification and ranking improvements deliver measurable gains in model quality.

Evaluation pipelines identify regressions and support reliable model releases.

Training, inference and data pipelines are robust, reproducible and maintainable.

Model improvements are driven by real-world feedback and measurable results.

Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.

How We Work

The best products in the world are built by small, world-class teams. We are a high-talent-density, hands-on team. We make decisions collectively and move quickly, balancing high-quality work with continuous learning.

Joining our team requires the ability to bring structure, exercise judgment and execute independently. Our goal is to put a truly magical product into the hands of our users.

Interview Process

If there appears to be a fit, we'll reach out to schedule 3, but no more than 4 interviews.

Applications are evaluated by our technical team members. Interviews will be conducted virtually and/or onsite.

We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us.

This overview was written from the original listing to help you understand the role. Always confirm the specifics on the employer’s application page.