Applied Machine Learning Scientist

The Applied Machine Learning Scientist will develop and deploy advanced machine learning solutions that improve advertising performance and maximize return on investment. Working within a highly technical engineering and data science environment, you will tackle complex optimization challenges across a large-scale platform processing millions of requests every second. You will combine statistical reasoning, optimization techniques, and machine learning expertise to design innovative algorithms that drive measurable business outcomes. From exploring new approaches and prototyping models to testing them against historical data and implementing production-ready solutions, you will contribute throughout the development lifecycle. You will collaborate with engineers and data specialists to build scalable systems capable of making billions of decisions efficiently. This remote-first opportunity is ideal for a technically strong, curious scientist who enjoys solving ambiguous problems and translating research into practical, high-impact applications.

• Education: Master’s degree or PhD in Computer Science, Statistics, Operations Research, or a related quantitative field. Additional degrees or interdisciplinary academic experience are considered an asset.

• Machine Learning Expertise: Comprehensive understanding of machine learning concepts and techniques, with the ability to apply theoretical knowledge to practical, real-world challenges.

• Statistics and Optimization: Strong knowledge of statistics, probability, mathematical optimization, and quantitative reasoning.

• Programming Proficiency: Strong coding skills and a solid understanding of data structures and algorithms, with the ability to write efficient, maintainable production-level code.

• Algorithm Development: Ability to design, prototype, test, and refine algorithms using historical data and experimental evidence.

• Analytical Problem-Solving: Strong analytical skills and the ability to investigate complex problems, evaluate alternatives, and draw meaningful conclusions from data.

• Independent Execution: Ability to take an ambiguous assignment, define the problem clearly, develop an actionable plan, and deliver results with appropriate autonomy.

• Research-to-Production Mindset: Interest in translating machine learning research and experimentation into scalable, reliable solutions used in production environments.

• Collaboration: A team-oriented approach and willingness to work closely with engineers, data scientists, and other technical colleagues in a supportive, collaborative environment.

• Adaptability and Curiosity: Enthusiasm for exploring new techniques, learning continuously, and addressing challenging technical problems in a rapidly evolving field.

• Remote Work Readiness: Ability to collaborate effectively in a remote-first organization and contribute to distributed technical teams.

• Quantitative Academic Foundation: An undergraduate degree in Computer Science, Engineering, Mathematics, Statistics, Physics, or a closely related quantitative discipline is relevant to the role.