A Machine Learning Engineer Who Shapes The Future

Machine Learning Engineer Opportunity About the Product: We're a leading school search platform dedicated to making researching and enrolling in schools easy, transparent, and free. Our mission is to provide comprehensive profiles on every school and college in America, 140 million reviews and ratings, and powerful search tools that help millions of people find the right school for them. Our goal is to create an inclusive community where everyone can thrive professionally. We strive to be a place where people enjoy working and can make a meaningful impact. About the Role: We're seeking a highly skilled Machine Learning Engineer to establish and lead our machine learning initiatives. This critical role will involve shaping the future of data science and ML at our organization. You'll be responsible for identifying high-impact opportunities, designing, building, and deploying machine learning models that drive business growth and enhance user experience across our platform. We're looking for a seasoned practitioner who's passionate about translating business challenges into data-driven solutions. You'll build, code, ship, measure, and have a proven history of deploying ML models into production environments with tangible results. You'll also possess the experience and desire to mentor future ML hires and establish best practices as our capabilities grow. Key Responsibilities: Collaborate closely with product, engineering, data analytics, and business stakeholders to identify and prioritize the most impactful ML opportunities that align with our strategic goals. Lead the end-to-end development of machine learning models – from data collection and feature engineering to algorithm selection, training, tuning, and validation. Develop production-grade code and systems to deploy, serve, and monitor ML models at scale, ensuring reliability and performance. Define key performance metrics, establish robust monitoring frameworks, analyze model performance in production, and drive continuous improvement through iteration and experimentation. Clearly communicate complex ML concepts, model behaviors, and results to both technical and non-technical audiences. First Year Plan: During the first month, you'll learn about our organization, meet with various team members, deep-dive into our platform, data architecture, and recommendation systems, and begin shaping a roadmap for high-impact ML opportunities. Within three months, you'll deploy your first machine learning model into production with robust monitoring and feedback loops, collaborate with product and engineering to define success metrics and integration strategies, and establish early ML development workflows, documentation, and performance monitoring. Within six months, you'll drive measurable improvements through experimentation and model iteration, introduce scalable MLOps practices to support deployment, retraining, and governance, and serve as a mentor and set engineering best practices for a growing ML function. Within twelve months, you'll lead ML efforts across multiple product areas, driving business impact at scale, influence company-wide strategy through technical leadership and ML evangelism, develop internal tooling, reusable frameworks, and scalable ML systems, and help grow the team through hiring, mentorship, and a culture of innovation. Requirements: We're looking for someone with 8+ years of professional experience in software development or data science, with at least 5+ years specifically focused on building and deploying machine learning models in a production environment. You'll need a demonstrable track record of successfully shipping multiple machine learning models that resulted in measurable business growth (e.g., increased user engagement, conversion rates, operational efficiency, revenue). You should have expertise in Python and common ML libraries/frameworks (e.g., scikit-learn, TensorFlow, PyTorch, Keras, XGBoost), a deep understanding of core ML concepts (e.g., classification, regression, clustering, recommendation systems, NLP, time series analysis, experimentation, model evaluation), strong SQL skills, and experience working with large datasets and data processing tools (e.g., Pandas, Spark). You'll also need experience with ML deployment patterns and MLOps principles (e.g., model serving, monitoring, CI/CD for ML, feature stores) and familiarity with cloud platforms (AWS, GCP, Azure). A strong ability to understand business needs, translate them into well-defined ML problems, and connect technical work back to strategic objectives is essential. Bonus Points: Experience building ML capabilities from the ground up, experience with recommendation systems, search ranking algorithms, or NLP applied to user-generated content, experience in the EdTech or consumer-facing platform space, familiarity with golang, express, Postgres, Snowflake, DBT, and Tableau, and contributions to open-source ML projects or publications in relevant conferences/journals are desirable. Benefits: We offer flexible Paid time off (PTO for any reason, including sick days), a personal laptop, health/sport budget, and fully remote work arrangements. We welcome new ideas and allow you to make an immediate impact on the team. Our culture values innovation, collaboration, and growth.