Software Engineer, Machine Learning Platform

Software Engineer, Machine Learning Platform

Coinbase has built the world's leading compliant cryptocurrency platform serving over 30 million accounts in more than 100 countries. With multiple successful products, and our vocal advocacy for blockchain technology, we have played a major part in mainstream awareness and adoption of cryptocurrency. We are proud to offer an entire suite of products that are helping build the cryptoeconomy and increase economic freedom around the world.

There are a few things we look for across all hires we make at Coinbase, regardless of role or team. First, we look for signals that a candidate will thrive in a culture like ours, where we default to trust, embrace feedback, disrupt ourselves, and expect sustained high performance because we play as a championship team. Second, we expect all employees to commit to our mission-focused approach to our work. Finally, we seek people with the desire and capacity to build and share expertise in the frontier technologies of crypto and blockchain, in whatever way is most relevant to their role.

As a software engineer on the Machine Learning and Platform team, you will have a chance to work across the full spectrum of our ML tech stack —  from batch and streaming feature aggregation pipelines that leverage many of our data infrastructure to our in-house-built model lifecycle management platform, Nostradamus, enabling ML Engineers to iterate on, train at scale and productionize models at lightning speed.

Responsibilities:

  • Boost ML Engineers’ productivity and enable fast model iteration cycles by building robust ML infrastructure, reusable tooling for feature discovery, data quality checks and model observability.
  • Feature Store: Batch and real-time data aggregation and serving layer that powers predictive models.
  • Nostradamus: Training and scoring infrastructure that enable machine learning practitioners to take a model from an idea to production.

Requirements:

  • 6+ years of engineering experience
  • Exhibit our core cultural values: add positive energy, communicate clearly, be curious, and be a builder.
  • Experience building backend systems at scale with a focus on data processing/machine learning/analytics
  • Experience building microservices
  • Experience with Python, Go and/or Java/Scala

Preferred (not required):

  • Computer Science or related engineering degree
  • Deep knowledge of Apache Flink, Spark, Airflow, Kafka/Kinesis, Snowflake, Hadoop, Hive

What to send:

A resume that describes machine learning models you've built and their business impact.

ID: G1660

Commitment to Equal Opportunity

Coinbase is committed to diversity in its workforce and is proud to be an Equal Opportunity Employer.  All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, sex, gender expression or identity, sexual orientation or any other basis protected by applicable law. Coinbase will also consider for employment qualified applicants with criminal histories in a manner consistent with applicable federal, state and local law.  For US applicants, you may view Pay Transparency, Employee Rights and Equal Employment Opportunity is the Law notices by clicking on their corresponding links.  Additionally, Coinbase participates in the E-Verify program in certain locations, as required by law.

Coinbase is also committed to providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please send an e-mail to accommodations@ coinbase.com and let us know the nature of your request and your contact information.  For quick access to screen reading technology compatible with this site click here to download a free compatible screen reader (free step by step tutorial can be found here). Please contact accommodations@ coinbase.com for additional information or to request accommodations.

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