> Markdown version of [/jobs/ext/3658193-senior-staff-machine-learning-engineer-ml-understanding](https://www.wearedevelopers.com/jobs/ext/3658193-senior-staff-machine-learning-engineer-ml-understanding). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Staff Machine Learning Engineer, ML Understanding - **Company:** The Ladders - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $266,000.0 - **Contract:** Permanent contract - **Skills:** Machine Learning, Recommender Systems, Large Language Models, Machine Learning Operations - **Published:** October 9, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/ppzzi3tizg ## About the Role * 10+ years of experience building and scaling production-grade ML systems, especially with user modeling and recommender systems. * Proven experience leading high-impact projects from concept to production. * Strong focus on real-world metrics, including engagement and retention. * Solid understanding of mainstream user modeling ML techniques and their practical applications. * Experience applying LLMs or foundation models to enhance existing systems. ## Description This role will shape the strategy and technical direction for user understanding at scale, with a focus on personalization, embeddings, and LLM-driven models. You will partner closely with platform and product teams to improve how user behavior is modeled and how those insights translate into better experiences across the company's products. The work is highly cross-functional and will have measurable impact on engagement, retention, and the performance of large-scale learning systems., * Define and implement a comprehensive user understanding framework incorporating embeddings and LLMs. * Design advanced user models for improved personalization across the company's products. * Utilize LLMs to enhance user profiles and deepen understanding of user behavior. * Collaborate with platform teams to build robust infrastructure for large-scale learning and serving. * Drive cross-team integration and measurement of user model impact across multiple product teams.