Machine Learning Engineer

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/ $150000 - $200000 annum

INFO

Salary
SALARY:

$150000 - $200000

Location

LOCATION

Job Type
JOB TYPE

Permanent

About the Role

Join a YC-backed seed-stage startup as an early ML hire, owning end-to-end model development for underwriting, risk scoring, and credit decisioning. You'll work directly with founders to build production models that drive revenue and portfolio performance.

What You'll Do

  • Design, build, and deploy production ML models in Python for underwriting, risk scoring, and credit decisioning.
  • Develop an elastic underwriting model that adapts to platform changes, cash flow dynamics, and evolving user behaviors.
  • Implement feedback loops to analyze performance month-over-month, continuously refining risk thresholds and model features.
  • Own the full ML lifecycle: data exploration, feature engineering, training, deployment, monitoring, and iteration.
  • Integrate models with the TypeScript backend (Node.js/Express, Next.js, React Native) to power decisioning flows.
  • Build high-quality datasets using financial data from Plaid, Stripe, and PostgreSQL (via Supabase).
  • Contribute to ML infrastructure design on GCP, including model serving, orchestration, and observability.

Tech Stack

  • Backend: Node.js/Express, Next.js, React Native (TypeScript)
  • Data: PostgreSQL (Supabase), GCP
  • Integrations: Plaid, Stripe
  • ML: Python-based modeling and deployment

What We're Looking For

  • 2-4 years of professional experience deploying ML models to production.
  • Strong Python skills with ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost).
  • Experience with risk scoring, credit decisioning, fraud detection, or similar applications.
  • Familiarity with threshold optimization, calibration, and precision/recall tradeoffs in risk contexts.
  • Comfortable working with PostgreSQL and cloud platforms (GCP preferred).
  • Thrives in fast-paced, ambiguous environments with end-to-end ownership and quick iteration.
  • Strong collaboration and communication skills across technical and non-technical teams.

Nice to Have

  • Experience with Plaid, Stripe, or other financial data APIs.
  • Background in fintech, lending, or credit underwriting.
  • Experience building data/ML pipelines in early-stage startups.
  • Experimentation experience (A/B testing, holdout validation).

CONTACT

Gabriella Varela

Recruitment Consultant

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