Machine Learning Engineer

S23 - AI

Machine Learning Engineer

Location: Seattle, WA (On-site, 5 days a week)

Salary: $120,000 - $220,000 Base + Equity (ML Engineer to Staff ML Engineer)

About the Role

A Seattle fintech is looking for a Machine Learning Engineer to own the underwriting models behind its core product. The platform makes 40,000 to 50,000 live credit decisions a month, each on a one to two second latency budget. The models read a user's bank transaction history, score the risk and make the call.

The team is small enough that one person owns a model from ETL to deployment and monitoring. Volume is growing fast and the company is targeting 5x growth over the next 12 months. Nobody is handing you a notebook to tidy up. You'll build the models that run the business.

Key Responsibilities

• Train and deploy underwriting models that score risk from bank transaction and user behaviour data
• Own the full model lifecycle: ETL, feature engineering, training, deployment, monitoring and iteration
• Ship real-time inference that returns decisions in one to two seconds at growing volume
• Work across fraud detection, risk underwriting and predictive analytics for payouts and repayments
• Help set the standard for ML practice as the team grows

What We're Looking For

• 1 to 6 years of full-time ML experience
• Models you've trained and put into production yourself, not just consulted on
• Experience with large-scale transaction or behavioural data
• At least one full-time role at a startup or small team
• Strong Python and SQL, with a real training stack (XGBoost, PyTorch or TensorFlow)
• Fintech underwriting, lending or fraud experience is a strong plus, not a requirement
• Open to visa transfers (OPT, H1B)

Why Join?

• Full ownership of models that make live financial decisions at scale
• A strict meritocracy: deliver and you move up fast, whatever your tenure
• Hiring at both ML Engineer and Staff level
• Competitive equity, plus medical, dental, vision, 401k and life insurance
• A mission-driven fintech helping underserved Americans manage their money