🛠 Tech Stack

💼 About This Role

You'll join the Models team to build and maintain ML training infrastructure for ad-targeting models, owning projects from ideation to production. Enable data scientists with reusable pipeline components and contribute to deep learning development using PyTorch and LightGBM. Collaborate with infrastructure teams on scalability and reliability topics.

🎯 What You'll Do

• Take end-to-end ownership of highly visible ML projects from ideation to production.
• Build and maintain quality data and ML pipelines for performance and cost efficiency.
• Enable Data Scientists with reusable, well-tested pipeline components and code review.
• Contribute to deep learning development including GPU orchestration and custom training loops.
• Maintain reproducibility and versioning across the ML lifecycle.

📋 Requirements

• 5+ years of experience as an ML Engineer or similar role.
• End-to-end ML Ownership including problem framing, experimentation, and deployment.
• Strong Python proficiency for ML pipeline code and tooling.
• Practical experience with scikit-learn, LightGBM, and PyTorch for deep learning.

✨ Nice to Have

• Experience with model serving infrastructure (real-time or batch inference).
• Familiarity with feature store and training-serving skew concepts.

🎁 Benefits & Perks

• 🏖️ Excellent benefits depending on country of residence.

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Team Interview· 60 min