Franklin Fitch
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.
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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