✦ Luna Orbit — AI & Machine Learning

Forward-Deployed Data Scientist

at Braze

📍 San Francisco Onsite Posted March 19, 2026
Type Full-Time
Experience mid
Exp. Years 3-5+ years
Education Bachelor’s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field
Category AI & Machine Learning

This role involves developing and deploying reinforcement learning algorithms, improving product AI capabilities, and collaborating with customer teams to ensure successful implementation of machine learning models.

  • Collaborate on implementations
  • Improve architecture
  • Refine RL algorithms
  • Develop reusable data pipelines
  • Contribute to product strategy

The position requires expertise in Python, ML pipelines, reinforcement learning, and deploying models using cloud and container orchestration tools like Kubernetes and GCP.

The ideal candidate is a mid-level data scientist or ML engineer with 3+ years of experience working with large-scale data, reinforcement learning, and deploying machine learning models in production environments. They should be proficient in Python and familiar with cloud and container orchestration tools.

PythonML pipelinesmodel deploymentreinforcement learning
DevOps toolsKubernetesTerraformGCP
GitCI/CDAirflowKubernetesTerraformGoogle Cloud Platform
PythonPandasTensorFlowKerasscikit-learnreinforcement learningML pipelinesmodel deployment
PythonPandasTensorFlowKerasscikit-learnXGBoostCatBoostML pipelinesmodel deploymentreinforcement learning
collaborationproblem-solvingcommunicationadaptabilityteamwork
Industry Technology
Job Function Advance AI and machine learning capabilities through research, development, and deployment
Role Subtype AI & Machine Learning
Tech Domains Python, TensorFlow, Keras, scikit-learn, XGBoost, reinforcement learning
pythonpandastensorflowkerasscikit-learnxgboostcatboostreinforcement learningML pipelinesmodel deploymentdata scientistmachine learning engineerGCPKubernetesTerraformDevOpsAIMLdata analysisXGBoost

Lack of experience with reinforcement learning, No proficiency in Python, No experience with ML pipelines or model deployment, Inability to work onsite in San Francisco

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