Wholesail

Wholesail

Founding Machine Learning Engineer

San Francisco, California · Exec

Sponsorship not specifiedDetected 74 days ago
PythonRailsExpressDistributed SystemsSQLMachine LearningTensorFlowPyTorchscikit-learnPandasData AnalysisData EngineeringLLMsAgentic AIMLOpsStatisticsA/B TestingERPResearchLeadershipCommunicationUnderwritingRisk Modeling

About the role

  • Credit is the load-bearing beam of our network.
  • Every time a vendor ships goods before getting paid, someone is taking a risk - today it's the vendor, tomorrow it should be a third party at a fair price.
  • Getting that transfer right is what unlocks the next order of magnitude of sales across the wholesale economy, and the only way to get it right is to underwrite buyers more accurately than anyone else in the industry.

Responsibilities

  • The first MLE on this team gets to decide what we build with it.
  • Our models directly shape the terms buyers are offered and the losses Wholesail and our capital partners absorb.
  • As the founding MLE on the Risk Engineering & Capital Products team, you'll own credit risk modeling end-to-end - from problem framing and data pipeline design, through model development and validation, through production serving and monitoring.
  • You'll work directly with the hiring manager, our engineering and product leadership, and our hands-on advisor Dan Massoni (former Chief Commercial Credit Officer of American Express) to decide what to build, explore features to model and how you'll scale our approach risk scoring..
  • Design, build, and validate credit risk models (PD, LGD, EAD, fraud, exposure sizing, pricing) against our proprietary reciprocal-bureau data and external signals.
  • Data engineering. Build the pipelines, feature logic, and training datasets that your models (and future models) run on. At this stage, the MLE owns the full data path; over time we'll grow a team around you.
  • Productionization. Ship models into production systems where they make real decisions on real dollars. Own deployment, monitoring, drift detection, and iteration.
  • Excellent written and spoken English communication skills; ability to explain model behavior, tradeoffs, and limitations to non-ML audiences (product, ops, capital partners, leadership).

Requirements

  • 5+ years of experience building models for production use cases.
  • Proficiency in Python and the modern ML ecosystem (pandas, scikit-learn, PyTorch or TensorFlow, XGBoost/LightGBM, Jupyter, etc.).
  • Solid statistical reasoning - you can spot leakage, selection bias, label noise, and spurious correlations, and you know why your offline metric doesn't always predict online performance.
  • Track record of owning models end-to-end: not just prototyping in a notebook, but taking something from a problem statement to a production system that makes decisions.
  • Deep modeling skill: strong command of supervised learning on tabular data, including feature engineering, model selection, hyperparameter tuning, calibration, and rigorous offline and online evaluation.
  • Strong data engineering skills - comfortable owning ETL and feature pipelines end-to-end against real, messy production data (SQL and a modern data-processing stack).
  • Excellent written and spoken English communication skills
  • ability to explain model behavior, tradeoffs, and limitations to non-ML audiences (product, ops, capital partners, leadership).
  • The spirit of a team player who believes in fostering a healthy and supportive work environment.
  • Bonus Qualifications
  • Experience building or operating ML platform components - feature stores, model registries, training orchestration, online serving, monitoring/drift detection.

Nice to have

  • Direct experience in credit risk modeling - PD/LGD/EAD models, scorecards, underwriting models, exposure management, or collections modeling - particularly for SMB or subprime segments.
  • Experience in fintech, lending, payments, fraud, or insurance, or with regulated modeling environments (model risk management, adverse action, fair lending).
  • Backend engineering depth: comfortable writing production services, designing APIs, and reasoning about distributed systems that consume your models.
  • Strong data analysis background - fluency in exploratory analysis, experimentation, and translating data into product and business decisions.
  • Experience with LLMs and agentic systems applied to operational problems (document understanding, KYB, entity resolution, agent tooling).
  • Published research, Kaggle placements, or prominent OSS contributions in the ML or data ecosystem.
  • BA or BS in Computer Science, Statistics, Mathematics, a related technical field, or equivalent practical experience.
  • Advanced degree (MS or PhD) in a quantitative field.

Equal opportunity

  • Wholesail is an equal opportunity employer.
  • We celebrate diversity and are committed to creating an inclusive environment for all employees.
  • We do not discriminate on the basis of race, color, religion, national origin, sex, gender identity or expression, sexual orientation, age, marital status, veteran status, disability status, pregnancy, or any other characteristic protected by federal, state, or local law.
  • Employment decisions at Wholesail are based on qualifications, merit, and business needs.

Visa & Work Authorization

  • Listen to the Visa episode of the Acquired podcast to learn how credit card networks did this for retail trade.
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This listing is sourced directly from Wholesail's careers page and normalized into a canonical job model.