Springboard runs an online, self-paced, Machine Learning Engineering Career Track in which participants learn with the help of a curated curriculum and 1-1 guidance from an expert mentor.
Our mentor community – the biggest strength of our programs – comprises experts from the best organizations in the world. Our mentors range from engineers and researchers at premier companies (Netflix, Pandora, LinkedIn, Apple) to a wide variety of top-notch startups and research institutes.
If you are as passionate about mentoring as you are about machine learning, and can give a few hours per week in return for an honorarium, we would love to hear from you.
About the course:
We’re looking for mentors for our Machine Learning Engineering Career Track course. This 6-month course is primarily designed for Software Engineers who want to become Machine Learning Engineers.
As part of the course, students go through an intensive curriculum that’s based on the way real-world applications are created. They start with data collection and wrangling, work their way through data exploration, followed by a deep dive into Machine Learning and Deep Learning, covering various techniques and architectures. Finally, they learn to use various engineering tools to productize, scale and deploy applications. Throughout the course, they work on a Capstone Project whose ultimate goal is not just to solve a problem using ML/DL, but to create an actual running application that they can showcase to potential employers.
As a mentor, you will:
– Guide students through technical challenges they face and questions they have about the curriculum, assignments or Capstone Project
– Provide structured, constructive feedback on their assignments, Capstone Project and portfolio to help them build the skills and confidence to succeed both in job interviews as well as in the real world
– Bring your real-world experience and knowledge into your mentoring to show students how the concepts they’re learning are applied in real life
– Meet with each of your students for 30 minutes each week via a video call
As part of being a Springboard mentor, you get to directly change the lives of people who are following in your footsteps into Data Science, which is a highly rewarding and meaningful contribution for many of our mentors. In addition, you’ll receive the following benefits:
– Access to our mentor community. We are really proud of our mentor community, a network of Data Scientists and Machine Learning Engineers at top companies around the world (Netflix, Pandora, LinkedIn, Apple and so on). Our mentors help each other take their careers to the next level.
– Training and feedback on mentoring. Our Mentor Operations team will help you become a better mentor, both via training during onboarding, as well as by providing regular, specific feedback. Our mentors find this training highly valuable as they move up to leadership roles at their industry jobs
– Flexibility. You get to work on your own time, from anywhere in the world and work with as many students as your schedule permits.
– Honorarium. Mentorship is a paid role, and you get an honorarium per student you work with.
What we’re looking for:
– You’re comfortable in the entire lifecycle of building a Machine Learning application, from data collection and building models to deployment and scaling.
You are an experienced software engineer using Python and all of the Data stack i.e. scikit-learn, pandas, numpy/scipy, TensorFlow, Keras and other common Python libraries.
– You have deep expertise and real-world experience in a wide variety of Machine Learning and Deep Learning models, including linear/logistic regression, tree-based models, RNNs, CNNs and so on
– You have a strong understanding of software engineering best practices, including version control, testing, monitoring and debugging
– As a mentor, you are empathetic and have excellent communication skills, especially when providing constructive feedback
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.