Machine Learning Engineer, Fetch

• Are you capable of training and deploying a Transformer model but know when a simpler solution will do?
• Do you like knowing how model architectures translate to flops and the milliseconds off a server?
• Have you lost entire days debugging inscrutable CUDA errors?
• If you answered yes to these questions we’d love to hear from you
• Technical Skills:
• Excellent programming skills (we use a lot of Python in this problem space but proficiency in other languages are equally welcome)
• Experience training ML models using a Python framework like Pytorch, TF, etc
• Experience deploying a model in a production environment with significant traffic. We process 100s of events per second in our prod pipelines
• Experience working inside of a codebase that is meaningful in size (minimum thousand lines) over an extended period of time

Desirable

• The ML Engineering team embodies these values and works with a laser focused objective to enable intelligent systems for end users, internal stakeholders, and external partners
• We are looking for a Machine Learning Engineer Apprentice to contribute to this vision and reap the rewards of joining an exciting company in the high growth phase
• Among other things, Fetch uses multiple ML models to power every scan in the app (millions a day and growing), fight against fraudulent behavior, and drive recommendations for users
• Machine learning is core to our product and we’re working to make it an even bigger part of the company
• Your focus will be on the intersection of training ML models and deploying them to production
• MLE’s at Fetch are responsible for the full cycle of machine learning on a team
• This includes managing/cleaning/piping data, training models for iterative improvements, and deploying those models to production
• This will be done in collaboration with backend engineers and data scientists on a product team
• You’ll be expected to create value in a fast moving environment and that might mean at any given moment deep diving into one of these stages of the pipeline