Ready to be pushed beyond what you think you’re capable of?
At Coinbase, our mission is to increase economic freedom in the world. It’s a massive, ambitious opportunity that demands the best of us, every day, as we build the emerging onchain platform — and with it, the future global financial system.
To achieve our mission, we’re seeking a very specific candidate. We want someone who is passionate about our mission and who believes in the power of crypto and blockchain technology to update the financial system. We want someone who is eager to leave their mark on the world, who relishes the pressure and privilege of working with high caliber colleagues, and who actively seeks feedback to keep leveling up. We want someone who will run towards, not away from, solving the company’s hardest problems.
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Our work culture is intense and isn’t for everyone. But if you want to build the future alongside others who excel in their disciplines and expect the same from you, there’s no better place to be.
We are looking for an aspiring Machine Learning Engineer to join our team. The Coinbase Machine Learning team is committed to developing sophisticated ML models that make our platform more secure, expand usage of Coinbase through personalized recommendations, and improve the user experience for our customers. Our mission is to keep building scalable, adaptive, blockchain aware ML systems that help our users explore and discover new use cases for Crypto on Coinbase and on the blockchain.
The Machine Learning org is a part of our Platform Product Group. The Platform Product Group’s mission is to build a trusted, scalable, and compliant platform to maximize velocity, efficiency and quality. They build the foundations that can be used by multiple products and teams at Coinbase so that users can have a consistent and high-quality experience.
What you’ll be doing (ie. job duties):
- Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale
- Contribute to our ML models for Risk, work at the intersection of Blockchain and AI technologies
- Work with our senior engineers and collaborate with our product partners to identify and solve new use cases for ML on blockchain
What we look for in you (ie. job requirements):
- 2+ yrs of industry experience as a Machine Learning Engineer
- Solid software engineering skills
- Experience with coding on ML platforms (e.g., Tensorflow, PyTorch)
- Experience with basic ML techniques (e.g., supervised and unsupervised learning)
- Willingness to learn and adapt to new technologies and challenges
Nice to Have:
- Crypto-forward experience, including familiarity with onchain activity such as interacting with Ethereum addresses, using ENS, and engaging with dApps or blockchain-based services.
- Master's degree or PhD in Machine Learning, Computer Science or related field
- Interest in DNNs, GANS, GNNs and Time Series modeling is a plus
Job ID: GPML04US
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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 the Know Your Rights notice here. 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 contact us at accommodations[at]coinbase.com to 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).
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