Senior ML Engineer

Yubo; Paris; Hybrid; EUR 90,000 - 130,000; Full time

Job summary

Yubo is seeking a Senior ML Engineer to join our Platform Engineering team, reporting to Head of Platform Engineering. You will deliver ML initiatives across the company, turning ideas into reliable production systems, from model development to deployment, monitoring and continuous improvement.

You will work on ML systems spanning content moderation, recommendations, and safety domains, helping evolve the ML platform, improve reliability, and promote scalable engineering practices across teams.

Job description

Who we are

Yubo is the Social Discovery app to make new friends and hang out online. By eliminating likes and follows, we empower our users to create genuine connections and show up as their true selves.

We've pioneered a new way for Gen Z to socialize online, and with millions of active users, our goal is to redefine how we connect today and tomorrow.

Our team is international, multicultural and deeply committed to its mission. As the leading platform to socialize online, we have a special responsibility to build a safe digital space for our community. Safety is embedded in our DNA, and our proactive approach focuses on user protection, support, and education.

Join us in this exciting journey and help us shape the future of social interactions!

About this role

As Yubo continues to scale, Machine Learning is becoming a core production layer, powering critical systems across safety, recommendations, and product optimization.

What makes this role unique is both the scale and diversity of our data, and the level of maturity we are aiming to reach.

We process massive volumes of images, text, and real-time user interactions, across millions of users worldwide, creating a wide range of high-impact ML challenges, including:

• Content moderation (image, text, behavior)

• Recommendation systems and user engagement optimization

• Behavioral detection and trust & safety models

• Emerging use cases such as dynamic pricing and growth optimization

At the same time, our current ML stack is still evolving.
Legacy models are not fully integrated into pipelines, lifecycle management remains inconsistent, and our approach can sometimes resemble "develop, deploy, and forget."

As ML usage expands across the company, this creates increasing complexity and dependency on reliable, well-structured systems.

There is still a huge amount of untapped potential, with many ML use cases yet to be designed, tested, and scaled, but unlocking it requires building a more robust and scalable ML operating model.

We are therefore looking for a Senior ML Engineer to join our Platform Engineering team, reporting directly to Mikael (Head of Platform Engineering).

You will play a key role in delivering ML initiatives across the company, helping turn promising ideas into reliable production systems. You'll work on everything from model development and experimentation to deployment, monitoring, and continuous improvement, while contributing to the evolution of our ML platform and engineering practices

Your responsibilities

ML Systems & Delivery

• Deliver end-to-end ML use cases (recommendation systems, safety algorithms, moderation models, etc.).

• Design, train, evaluate, and deploy ML models where appropriate.

• Balance speed of delivery with robustness and reliability.

ML Lifecycle & Reliability

• Drive improvements across the ML lifecycle (training, deployment, monitoring, iteration).

• Establish KPIs and monitoring standards to track model performance over time.

• Ensure continuous alignment with product and safety objectives.

• Help improve the reliability and observability of production ML systems.

Platform & Standardization

• Contribute to the evolution of our ML platform through tools, workflows, and reusable components.

• Help establish and promote scalable ML engineering practices across teams.

• Contribute to improving self-service ML capabilities where relevant.

Legacy & Advanced Use Cases

• Take ownership of legacy models and realign them with current business needs.

• Improve, retrain, and integrate them into modern pipelines.

• Contribute to best practices around LLM usage in moderation, recommendation, and other product domains.

• Explore and implement advanced ML approaches where relevant.