remote
Staff Machine Learning Engineer - Credit Acceptance
ML Engineer
Lead end‑to‑end ML initiatives for automotive financing, building scalable models on AWS, deploying production pipelines, and driving data‑driven product decisions with Python and deep learning techniques.
About the role
Key Responsibilities
- Design, develop, and production‑grade deploy machine learning models that drive credit decisioning and fraud detection for automotive financing.
- Build and maintain end‑to‑end data pipelines on AWS (S3, Glue, Redshift, SageMaker) to ingest, transform, and serve high‑volume structured and unstructured data.
- Collaborate with cross‑functional teams to translate business requirements into scalable ML solutions, ensuring model performance, fairness, and compliance.
- Implement continuous integration/continuous deployment (CI/CD) workflows for model training, validation, and monitoring using Docker, Kubernetes, and SageMaker Pipelines.
- Conduct rigorous model evaluation, hyper‑parameter tuning, and A/B testing, and present findings to stakeholders.
Requirements
- 5+ years of experience in production ML engineering, with a strong background in Python and deep learning frameworks (PyTorch, TensorFlow).
- Proven expertise in AWS services (SageMaker, Glue, Redshift, Lambda) and container orchestration.
- Hands‑on experience building data pipelines, feature stores, and model monitoring solutions.
- Strong analytical skills, ability to translate complex data into actionable insights.
- Excellent communication and collaboration skills, comfortable working in a fast‑paced, cross‑functional environment.
Skills
pythonmachine learningawsdeep learning