ML Engineer (Product)

Overview This range is provided by Franklin Fitch. Your actual pay will be based on your skills and experience "” talk with your recruiter to learn more. Base pay range $160,000.00/yr - $200,000.00/yr We're looking for an ML Engineer who thrives at the intersection of modeling, engineering, and product. In this role, you'll design and deploy production-ready models, build scalable APIs for real-time decisioning, and develop the analytics layer powering the next generation of fintech applications. You'll report directly to the Director of Engineering and own projects end-to-end"”from research and prototyping through to large-scale deployment and monitoring. What You'll Do Develop, validate, and scale ML models for affordability, risk assessment, fraud detection, and predictive analytics Build and maintain robust data pipelines, feature stores, and APIs that bring ML into production at scale Collaborate with product, data, and engineering teams to deliver customer-facing features Experiment with different modeling and system approaches, with strong judgment on when ML is the right tool Drive innovation in model performance, monitoring, reliability, and explainability Implement MLOps best practices for versioning, testing, deployment, and monitoring Ensure compliance with data security, privacy, and regulatory requirements relevant to fintech applications Who You Are Strong product mindset with a passion for solving real-world customer problems Proficient in Python , modern ML frameworks (TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM), and data manipulation tools (Pandas, Spark, SQL) Skilled in building and scaling systems with cloud platforms (AWS, GCP, or Azure) and containerization/orchestration (Docker, Kubernetes) Experience with distributed systems and real-time data processing (Kafka, Flink, Beam) Knowledge of MLOps tools (MLflow, Weights & Biases, Kubeflow, SageMaker, Vertex AI, Airflow) Strong understanding of feature engineering, model monitoring, drift detection, and retraining strategies Familiarity with data privacy, fairness, and interpretability in ML High-quality standards with the ability to move fast, experiment, and iterate Excited about working in a high-ownership, early-stage startup environment where you'll shape both technology and product direction Seniorities & Employment Mid-Senior level Full-time Job function: Finance and Information Technology Note: This listing includes general information and does not guarantee a position. Refer to Franklin Fitch for official details. #J-18808-Ljbffr

Created: 2025-10-01