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# Senior ML Engineer

- **Company:** YUBO LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Neural Networks, Big Data, Information Engineering, Dataspaces, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Azure Machine Learning, Pytorch, Large Language Models, Generative AI, Backend, Machine Learning Operations, Code Restructuring
- **Published:** October 16, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/ps079nt9p8

## About the Role

* 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.

## Description

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, * 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