Who we are

Yubo is the instantly social, social app: the leading real-time social discovery platform where you can meet new people for friendship, crushes and community. Our mission is to help young adults find community - whoever they are, wherever they are and whenever they feel social. That's why we’re building a more spontaneous and human social world, one based on interaction, exploration and possibility.

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.

We also work closely with the broader technology industry to share our knowledge and NGOs create industry-leading child protection standards.

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.

Cross-functional Collaboration

Partner with Data Engineering, MLOps, Backend Platform, and Product teams.
Act as a bridge between ML, platform, and business stakeholders.
Provide technical leadership on ML initiatives and help improve engineering practices across teams.
Share knowledge and help raise the overall ML maturity of the organization.

Our technical environment / ML scope

ML frameworks: PyTorch
Languages: Python (data stack)
Core topics: Neural networks, LLMs, data sampling
Use cases: Recommendation systems, safety algorithms, moderation
Ecosystem: Data Engineering, MLOps pipelines, Backend Platform

Who you are

You have 5+ years of experience in ML / Data, including work on large-scale datasets (datasets of hundreds of gigabytes).
You have strong expertise in modern ML frameworks (TensorFlow or PyTorch or JAX).
You are highly proficient in Python (data ecosystem).
You have strong knowledge of neural networks and practical experience with LLM-based systems.
You understand ML systems end-to-end (data → training → deployment → monitoring).
You have strong experience in production ML systems, not only research.
You demonstrate strong product sense and can align models with business needs.
You are pragmatic and impact-driven, balancing experimentation with delivery.
You are able to explain complex ML topics clearly (strong pedagogy).
You operate well under ambiguity and can structure complex problems.
You are able to drive technical decisions and influence stakeholders through expertise and collaboration.

In your first 6 months, you will

Deliver an end-to-end ML use case to validate impact and execution.
Audit and improve lifecycle management of existing ML models.
Refactor and retrain at least one legacy safety model.
Build reusable ML components that improve development efficiency across teams.
Improve monitoring and observability of production ML systems.
Contribute to the evolution of our ML platform and engineering practices.
Help move critical ML systems toward more reliable and measurable long-term operation.

The recruitment process

Phone screen with a Talent Acquisition Manager
Interview with Mikael (Platform Engineering Director)
Technical case study
Cultural fit assessments

What we offer

A highly competitive salary range as well as equity in the company
A highly flexible remote work policy, 2 days at the office per month, with monthly team events.
We also cover fees for external professional events and meetups (Android Makers, etc…)
Great health insurance coverage for both you and your family by Alan, fully paid for by Yubo !
Numerous benefits for parents: additional parental leave, easy access to nurseries and daycare facilities in France.

Our approach to privacy & safety

As part of your role, you may handle tools and features involving personal data. We expect all employees to demonstrate strong awareness of privacy and safety issues, and to actively support our Privacy & Safety by Design efforts.

Join Yubo and help shape the future of Social Discovery while enjoying a culture that values flexibility, well-being, and impact.

Here’s how we live our mission every day:

You own the impact: Step up, adapt, and make it matter

Unconventionally Smart: Hack with intent, borrow smart, build better

Be Bold & Resilient: Raise your head, break barriers, keep moving forward

One team, one mission: No egos, no passengers, just shared wins

Trust & Flexibility: Our hybrid model calls for only two office days a month; the rest is up to the rhythm that works best for you.

Enjoy Top-notch Benefits

Culture is central at Yubo, hence the numerous benefits:

Cool Workplace: enjoy our amazing Parisian office and our many hybrid work options

Team Activities: participate in get-togethers, events, and team-building activities

Family-Friendly: we support parents with childcare options and family-friendly policies

Wellness Programs: benefit from comprehensive health insurance, wellness programs, sports classes, and mental well-being initiatives

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