Staff Machine Learning Engineer

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 Staff 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 technical leadership 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.

Collaboration with Cross-Functional Teams: Work closely with a strong team of engineers, ml infra team, data scientists and product managers to build scalable and high-impact machine learning systems. Collaborate on the end-to-end development process, from ideation to deployment, ensuring that data-driven solutions are seamlessly integrated into our products and services.

Innovation: Develop a long-term technical vision; propose a roadmap for team setting clear objections. Play a vital role in the design and implementation of new products and features, while also enhancing the existing product suite with innovative machine learning capabilities.

Mentorship: Guide junior team members and foster a culture of continuous learning and technical excellence. Lead and encourage knowledge sharing to enhance the team's capabilities in advanced machine learning techniques from both industry and academia.

Who you are:

You hold a Master’s or Ph.D. in a quantitative field, such as Computer Science, Statistics, Mathematics, or a related discipline.

You possess 6+ years of experience in data science and machine learning, ideally within the tech industry and marketplace environments.

You have hands-on experience post-training and self-hosting open-weight LLMs (e.g., fine-tuning, quantization, serving infrastructure such as vLLM, SGLang ) rather than relying solely on third-party APIs.

You have a deep understanding of evaluation metrics across deep learning and classical ML and can design evaluation strategies that catch regressions before they reach production.

You have end-to-end fluency in the MLOps lifecycle — data versioning, feature stores, CI/CD for ML, model monitoring/observability, and retraining pipelines — not just model development.

You are knowledgeable about large-scale distributed application architecture, design, implementation, and performance tuning.

You have the ability to drive the roadmap and direction of scalable, production-quality systems end-to-end, and effectively communicate cross-functionally with Product and Engineering teams.

You have practical knowledge of advanced machine learning algorithms and deep learning to deploy for search systems, information retrieval, and ranking algorithms.