Staff Machine Learning Engineer

About the role

As a Staff Machine Learning Engineer at Everyday Labs, you will architect and deliver the intelligence that powers our next generation of products, experiments, and ventures. You will work at the intersection of applied machine learning, experimentation, and scalable engineering — building models and ML infrastructure that can go from idea to deployed value with exceptional velocity and rigor. This is a highly technical, high-ownership role where you will influence technical strategy, hire and mentor engineers, and help shape the culture and systems that allow ML ideas to become high-impact reality.

What you’ll do:

Machine Learning: Turning Ideas into Value • Design, train, and deploy ML models that deliver measurable impact across multiple aspects of noon’s vast e-commerce footprint. • Translate ambiguous or zero-to-one problems into clear hypotheses, measurable signals, and verifiable ML solutions. • Collaboration with product, engineering and business leaders to define value metrics and success thresholds early and rigorously. Production ML Systems & Pipelines • Architect reliable training, validation, and deployment pipelines using modern MLOps practices. • Build and maintain feature stores, automated retraining systems, online inference services, and monitoring frameworks. • Ensure systems meet high bars for observability, reliability, and performance — excellence in execution is part of our signature. Technical Leadership & Cross-Team Ownership • Hire and mentor ML and software engineers across Labs in traditional ML, generative AI technologies. • Contribute to cross-initiative architecture, documentation, and open knowledge sharing. • Work noon partners and stakeholders to apply ML where it can unlock disproportionate impact. What you’ll need: • 6+ years of experience in applied ML engineering • Bachelor’s degree in Computer Science, Engineering, or a related technical field, master’s or PhD is a plus but not required. • Strong proficiency with Python, modern ML frameworks (PyTorch, TensorFlow, Scikit-learn, XGBoost), and model lifecycle best practices. • Proven experience deploying ML models into real-world systems with CI/CD and automated monitoring. • Strong SQL and data engineering fundamentals, including building scalable data pipelines (Airflow or similar). • Experience with cloud platforms (AWS, GCP, Azure). GCP is a plus. • Experience with generative AI, LLMs, or NLP systems is a strong bonus.