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Machine Learning Engineer II Recommendation Systems- Credit KarmaIntuit • Oakland, California, United States
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Machine Learning Engineer II Recommendation Systems- Credit Karma

Intuit
  • US
    Oakland, California, United States
  • US
    Oakland, California, United States
Postuler Maintenant

À propos

The Data & AI Platform (DAP) team at Intuit Credit Karma builds and operates the ML infrastructure that powers personalized financial recommendations for over 100 million members. Our platform spans feature engineering, data systems, model training and serving, and ML lifecycle management. We partner closely with data scientists and product engineers to turn research into production-grade AI at scale.
As an MLE II on the DAP team, you will be a hands‑on contributor who builds reliable, well-tested data and ML pipelines end-to-end. You will collaborate with data scientists to prepare training datasets, train and evaluate machine learning models, and deploy them into production. This role emphasizes strong execution on well-scoped technical problems and a growing ability to identify and solve ambiguous issues with guidance from senior engineers.
Responsibilities
Write data ETL code to read, clean, and transform raw feature sets (e.g., visitor session logs) into enriched training‑ready datasets.
Perform feature enrichment through aggregation logic such as computing rolling averages, most‑recent values, and merged histories across identity‑stitched records.
Handle data quality issues including null values, sparse features, and identity resolution across multiple visitor IDs (e.g., ivid/super_ivid stitching).
Integrate enriched features with label sets using point‑in‑time‑correct joins to prevent information leakage in training data.
Train binary classification models using libraries such as scikit‑learn, XGBoost, or TensorFlow on properly assembled training sets.
Perform feature engineering for model input (encoding categorical variables, handling missing data, scaling numerical features).
Evaluate model performance using appropriate metrics (AUC, precision, recall, F1) and communicate results clearly.
Iterate on model quality through hyperparameter tuning and feature selection.
Build scalable data pipelines in a big data environment using tools such as BigQuery, Apache Beam/Dataflow, Spark, Flink and Airflow.
Implement models into production serving infrastructure and support model refresh workflows.
Monitor production model performance and data quality; triage and resolve issues.
Collaborate with data scientists to translate experimental notebooks into maintainable, production‑grade code.
Partner with data scientists, platform engineers, and product teams to deliver ML‑powered features.
Participate in code reviews, design discussions, and on‑call rotations.
Proactively learn the team’s domain (feature platforms, ML lifecycle, recommendation systems) and contribute to documentation.
Qualifications
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field.
3+ years of professional experience in software engineering, data engineering, or machine learning engineering.
Strong proficiency in Python; experience with data libraries (pandas, NumPy, scikit‑learn).
Solid understanding of data structures, algorithms, and writing clean, testable code.
Experience with SQL and working with large‑scale datasets.
Familiarity with the end‑to‑end ML workflow: data preparation, feature engineering, model training, evaluation, and deployment.
Ability to write bug‑free, executable code for data ETL and model training tasks within a structured timeframe.
Clear communication skills; able to articulate a technical plan before coding and explain decisions.
Preferred Qualifications
Experience with big data tools such as Apache Spark, BigQuery, or Dataflow.
Familiarity with ML frameworks beyond scikit‑learn (e.g., TensorFlow, Keras, XGBoost, PyTorch).
Experience with workflow orchestration tools (Airflow, Kubeflow, TFX).
Exposure to feature stores, feature platforms, or streaming feature computation (e.g., Chronon, Feast).
Experience with GCP services (AI Platform, Dataproc, Composer, BigTable).
Understanding of model serving patterns (batch scoring, real‑time inference, A/B testing).
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs. Pay offered is based on factors such as job‑related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. The expected base pay range for this position is: $140,500-$190,000.
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  • Oakland, California, United States

Compétences linguistiques

  • English
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