Building credit and cash-advance tools for the millions of Americans traditional banking overlooks.
Machine Learning Engineer
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We're expanding a lean ML team inside a fintech company that already moves real money to real people — advances that land in under a minute. This role sits at the intersection of risk modeling and product engineering: you'll design models that price short-term liquidity, detect fraud in near-real-time, and surface credit signals from thin-file applicants. Your work will ship inside a fast, GCP-hosted stack where Go services and MySQL pipelines meet PyTorch and TensorFlow inference. The roadmap ahead includes underwriting automation, repayment-prediction systems, and cash-flow-based scoring that opens doors for borrowers who've been locked out for too long.
What they're looking for
- 1–6 years building and deploying machine learning models in production, ideally on customer-facing financial or risk products
- Strong Python fluency and hands-on experience with at least one deep learning framework (PyTorch or TensorFlow)
- Comfort owning the full model lifecycle: data exploration, training, evaluation, containerized deployment, and monitoring
- Working knowledge of relational databases (MySQL) and cloud infrastructure (GCP preferred)
- A clear-eyed approach to fairness, explainability, and the real-world stakes of lending algorithms