In a machine learning engineering job, you will build machine-learning-based systems, tools, and services that serve as infrastructure for our internal and external clients. You will be responsible for innovations in how we use technology, machine learning, and data in order to enable the productivity of our clients. You will help scale up algorithms, process huge data sets, and train models effectively. You will work side-by-side with our data scientists, complementing their role and serving as an essential connection to our software engineers. Much of your work will be client facing and on-site.
BCG GAMMA machine learning engineers have the opportunity to work with clients across a wide range of global industries, solving technical challenges within various business problems, such as demand forecasting, predictive maintenance, personalization, churn prediction, computer vision, natural language processing, and many others. Your work will deliver more direct impact than that of traditional machine learning roles as you help envision, build, and develop our next generation of data engines and tools—fundamentally transforming our clients’ businesses. And you will serve to bridge the gap between business and engineering, functioning with deep expertise in both worlds.
During your career in machine learning engineering, you will learn how to interact with clients, collaborate with a wide variety of technical and nontechnical professionals, manage your own teams, and become a trusted advisor to large companies’ product development and IT departments. You will gain scope and responsibility at a quick pace, with plentiful promotion opportunities and the potential for fast career growth.
We are looking for people with an engineering or computer science degree and three to five years of experience in algorithms, data structures, and object-oriented programming—along with a strong understanding of machine learning fundamentals and deep learning frameworks.
Our machine learning engineers have implementation experience in machine learning algorithms and applications, as well as knowledge of or experience in building production-quality and large-scale deployment of applications related to machine learning. They also have strong programming skills in at least one object-oriented programming language and fluency in at least one of the modern distributed machine learning frameworks.