Sr. Machine Learning Engineer
Back River Search
- San Francisco, CA
Our client is dedicated to helping our members lead healthy financial lives. That’s why we offer an award-winning bank account that doesn’t charge unnecessary fees, gives members early access to their paychecks, and helps them save money automatically. Hundreds of thousands of people use their mobile app and debit card to make purchases, track spending, save for the future, and more.
They believe the big banks fail to help their members achieve financial health - and in many cases work against it, charging hundreds of dollars in hidden fees and pushing products that drive people into debt. They don’t think it needs to be this way, so we’re out to beat them.
They have one of the most experienced management teams in Fintech and just raised a $70M Series C funding round, led by Menlo Ventures, to fuel their growth. If you’re looking to join a small but fast-growing company with a beloved, daily-use product and an authentic mission that puts people first, we want to meet you.
Our client is a technology and data-driven consumer bank. We are amassing vast amounts of data that we want to use to ensure the best practices in risk management, new user acceptance, information security, underwriting, and more. Our ML and Data Science team occupies a critical role in the company, creating models and infrastructure that allow us to evaluate events in realtime in new, efficient, and accurate ways so as to minimize fraud and scale our ability to manage risk.
What You’ll Do
In this role, you’ll build and implement novel Machine Learning and Deep Learning systems to combat fraud on our platform as well as help building the infrastructure to train and deploy them. Specifically, you will:
- Design and implement the infrastructure required to train models at scale.
- Implement ML/DL models that fight fraud and minimize the company’s risk
- Work with the data team’s infrastructure to build real time and offline feature databases
- Work with the data team to create the infrastructure to build and maintain the datasets from which models are created
- Build the model serving systems with which we can deploy our models to production
- As we grow, scale the ML system to be able to support more use cases and ML model types.
Qualifications & Requirements
- One of the following: (a) MS in CS or related field with 3+ years of experience in implementing and deploying large scale ML solutions OR (b) PhD in Machine Learning, Statistics, Optimization, Physics, or related field, with 1+ years experience building production-ready ML models and systems
- 3+ years industry experience developing machine learning models at scale from inception to business impact. Leadership opportunities are also available.
- 5+ years of building distributed systems and/or scalable backend systems and the ability to maintain such systems in production.
- Strong software engineering fundamentals - understanding of data structures and algorithms, O-notation, ability to maintain a test suite and write clear maintainable code.
- Familiarity with majority of the following tools: Tensorflow, Numpy, Scipy, SparkML, pandas, scikit-learn.
- Demonstrated leadership and self-direction, and willingness to both teach others and learn new techniques
- Experience with big data processing and storage systems: Hadoop, Spark, Hbase, Cassandra etc.
- Strong programming skills in Python. Intermediate to Advanced knowledge of SQL and ability to wrangle data from many disparate data sources
- Technologies we use: MySQL, Python, AWS, Snowflake,R, and Looker, among many others
Monday, July 8, 2019