Senior Machine Learning Engineer

About this role

For over 30 years, Angi has powered the future of the home services industry, creating an environment where homeowners and pros benefit from more jobs done well. For homeowners, our platform is a reliable way to find skilled pros. For pros, we're a reliable business partner who helps them find the winnable work they want, when they want. For employees, we're an amazing place to call home. We can't wait to welcome you.

Angi at a glance: - Founded in 1995 as Angie’s List and rebranded in 2021 - Global company with 9 brands in 8 countries and employees worldwide - Homeowners have turned to us for 300 million home projects and counting About the role: Angi is seeking an exceptional Senior Machine Learning Engineer to join our Data Science and Machine Learning team, playing a pivotal role in transforming our platform into a world-class online marketplace.

This position involves tackling complex challenges such as homeowner-pro search ranking and leveraging predictive models to enhance our product and consumer experience. The ideal candidate will apply state-of-the-art machine learning and AI techniques to solve Angi’s marketplace problems, demonstrating proficiency in software engineering. Additionally, the role requires close collaboration with the platform team to deploy models and services at scale with low latencies, ensuring seamless integration and high performance.

What you’ll do: - Model Development: Lead development of advanced machine learning and AI models to improve our marketplace algorithms (e.g. search ranking, recommendation and matching solutions). Success in these areas will impact user experience & engagement, retention, and conversion rates - critical metrics for business success. - Model Deployment and Engineering: Design and architect robust MLOps practices to ensure the seamless deployment and scalability of machine learning models, including self-hosted large language models (LLMs).

This includes automating model training and post-training (fine-tuning, RLHF/preference alignment, distillation), optimizing runtime performance and inference cost of models, and owning the full MLOps lifecycle — from data pipelines and experiment tracking through CI/CD, model registry, monitoring, and rollback — to enable fast, reliable delivery of machine learning solutions into production environments. - Model Evaluation: Define and own rigorous evaluation frameworks for deep learning and ML systems — offline metrics , online experimentation (A/B testing, guardrail metrics), and LLM-specific evaluation (hallucination rate, task accuracy, human/LLM-as-judge scoring) — to ensure models meet quality and safety bars before and after deployment.