Machine Learning Engineer, Deployment - San Francisco, CA, United States - Cred Protocol Inc.

Description

Improving access to financial resources means quantifying risk. Quantified risk at scale is credit scoring.

Cred Protocol's building one of the first decentralized credit scores, a DeFi primitive that unlocks capital-efficient lending and enables under-served communities to access the power of DeFi lending.

We're funded by top crypto investors including AllianceDAO, Volt Capital, Robot Ventures, GSR Markets, DCG Expeditions, SNZ Holding, AngelDAO, imToken and amazing founders and executives from across web2 and web3.

We're a team of prior founders with successful exits and deep expertise in massively scalable infrastructure, AI, and blockchain with experience spanning Silicon Valley startups and world-class institutions such as Facebook/Meta, Amazon, Deutsche Bank and Cambridge University.

As our first Machine Learning Engineer, you'll help establish a foundation for ML that takes us from initial MVP to scale.

Working closely with our data engineering team, you'll design an implement an enterprise-grade machine learning infrastructure focused on credit risk modeling and fraud detection based on blockchain data.

You'll be responsible for feature engineering, model training, deployment into production and iteratively testing and improving models over time.
Create credit risk models that correlate on-chain activity with DeFi loan liquidations.
Create fraud detection models that mitigate bad-actors.

Help design and implement our machine learning operations infrastructure that enables us to iterate, improve and streamlines production deployment.

Inform and help prioritize our machine learning platform roadmap.

Communicate and coordinate across related functional areas such as Data Science, Data Engineering, Web3 Engineering (Smart contract and Oracle engineering).

Inform and educate internal and external stakeholders through writing and speaking engagements.

Excited by decentralized finance's potential to rebuild traditional finance on blockchain rails that are fundamentally fairer and more transparent.

3+ years hands-on experience implementing machine learning and deep learning .
3+ years using agile development methodologies.
Bonus] Experience training XGBoost machine learning models + deploying to production.
[Bonus] Experience training Graph Neural Network models.
[Bonus] Hands-on smart contract development (on Ethereum blockchain).
[Bonus] Experience working in fraud detection, lending or credit risk modeling.
Flexible hours.
Visa sponsorship where needed.