About the job
We are looking for a Senior Machine Learning Engineer to help evolve our large-scale recommendation systems and apply AI / LLM technologies to real-world production problems. You will work on core feed, retrieval, and ranking systems serving tens of millions of users, while also participating in AI-driven innovation projects that explore new ways to improve content discovery, personalization, and developer productivity. This role is ideal for engineers who enjoy strong individual ownership, hands-on development, and translating ideas into reliable systems.
Responsibilities
Design, build, and iterate on high-throughput, low-latency recommendation systems powering NewsBreak’s core feeds.
Improve retrieval, ranking, and multi-objective optimization to balance engagement, retention, content quality, and business metrics.
Own systems from offline training → online inference → A/B experimentation → metric analysis.
Identify and resolve issues related to data quality, model drift, and system performance in production.
Apply LLMs and foundation models to recommendation-related problems such as content understanding, user intent modeling, and feature generation.
Prototype quickly, validate with experiments, and push successful ideas into production.
Embrace AI-assisted development workflows, including LLM-powered coding, debugging, and analysis.
Apply AI across feature ideation, prototyping, and experiment analysis.
Contribute to 0–1 AI-powered projects, helping take ideas from early exploration to MVP and production.
Qualifications
Minimum
3+ years of industry experience building machine learning or recommendation systems in production.
Strong understanding of recommendation system fundamentals (ranking, embeddings, user/item modeling, experimentation).
Strong coding skills with Python / Java / Scala, and experience with PyTorch or TensorFlow.
Experience working with large-scale data and ML systems (e.g., Spark, distributed training, real-time inference).
Ability to own features end-to-end and deliver measurable business impact.
Preferred
Production experience applying AI or LLMs to recommendation systems.
Experience contributing to 0 → 1 AI or ML-driven products.
Familiarity with AI-assisted development tools and workflows.