Bree

2 months ago

Machine Learning Engineer, Underwriting

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Design, deploy, and scale end-to-end machine learning pipelines and MLOps for underwriting decisions. Work with structured and unstructured data, deploy models on cloud platforms with containerization, ensure monitoring and explainability, and collaborate with data engineers to deliver production-ready AI solutions that power Bree's fintech platform.

Location: United States

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference.
•Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies.
•Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques.
•Collaborate with data engineers to develop high-performance data pipelines for training and inference.
•Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes.

Pay and Benefits

Salary: USD 130,000 - 230,000 annually

Perks:Health InsuranceDentalVisionLearning BudgetHome OfficeWellness StipendMeal AllowanceCommuter BenefitsPaid ParentalPaid Leave

Key Requirements

•Proficiency in Python with experience in ML libraries such as Scikit-Learn, LightGBM, and PyTorch.
•Experience with MLOps tools (MLflow, Kubeflow, or SageMaker) for tracking experiments and automating workflows.
•Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL).
•Knowledge of deploying ML models to cloud platforms (AWS, GCP, Azure) and managing infrastructure with Docker and Kubernetes.
•Ability to design real-time and batch inference pipelines and maintain model performance with monitoring and explainability.

Experience:FintechConsumer finance

Skills:Problem-solvingAnalytical thinkingCollaborationOwnership

Languages:English

Tech Stack:PythonScikit-learnLightGBMPyTorchMLflowKubeflowSageMakerPandasNumPySQLNoSQLAWSGCPAzureDockerKubernetes

Eligibility

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

Bree

Develops Bree, an AI-powered writing assistant that helps individuals and teams generate, edit, and optimize emails, marketing copy, and short-form content to save time and improve messaging consistency.

Industry: AI & Machine Learning

Company Size: Small (11 to 50 employees)

Growth: Early Stage Startup

Website

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Bree

2 months ago

Machine Learning Engineer, Underwriting

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Location: United States

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference.
•Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies.
•Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques.
•Collaborate with data engineers to develop high-performance data pipelines for training and inference.
•Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes.

Pay and Benefits

Salary: USD 130,000 - 230,000 annually

Perks:Health InsuranceDentalVisionLearning BudgetHome OfficeWellness StipendMeal AllowanceCommuter BenefitsPaid ParentalPaid Leave

Key Requirements

•Proficiency in Python with experience in ML libraries such as Scikit-Learn, LightGBM, and PyTorch.
•Experience with MLOps tools (MLflow, Kubeflow, or SageMaker) for tracking experiments and automating workflows.
•Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL).
•Knowledge of deploying ML models to cloud platforms (AWS, GCP, Azure) and managing infrastructure with Docker and Kubernetes.
•Ability to design real-time and batch inference pipelines and maintain model performance with monitoring and explainability.

Experience:FintechConsumer finance

Skills:Problem-solvingAnalytical thinkingCollaborationOwnership

Languages:English

Tech Stack:PythonScikit-learnLightGBMPyTorchMLflowKubeflowSageMakerPandasNumPySQLNoSQLAWSGCPAzureDockerKubernetes

Eligibility

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

Bree

Industry: AI & Machine Learning

Company Size: Small (11 to 50 employees)

Growth: Early Stage Startup

Website