✦ Luna Orbit — Software Engineering

Senior Engineering Manager

at Braze

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

This role involves developing and deploying machine learning models, collaborating with customer teams, and extending product capabilities through data pipelines and reinforcement learning algorithms in a SaaS environment.

  • Collaborate with customer teams on ML use cases
  • Develop scalable data pipelines
  • Refine reinforcement learning algorithms
  • Contribute to product strategy
  • Ensure successful model deployment

The technical environment includes Python, TensorFlow, Keras, scikit-learn, SQL, Kubernetes, and Google Cloud Platform, focusing on large-scale data processing and model deployment.

The ideal candidate is a data scientist or ML engineer with 3-5+ years of experience, proficient in Python and ML libraries, with strong skills in building scalable data pipelines and deploying models in production environments.

3-5+ years experience as Data Scientist or ML EngineerProficiency in PythonExperience with ML libraries (TensorFlowKerasscikit-learn)SQLBuilding scalable data pipelines
Experience with DevOps toolsKubernetesGCPTerraformAirflow
PythonTensorFlowKerasscikit-learnSQLKubernetesGoogle Cloud PlatformTerraformAirflow
PythonTensorFlowKerasscikit-learnSQLML pipelinesmodel deploymentDevOpsKubernetesGCP
PythonTensorFlowKerasscikit-learnSQLML pipelinesModel deploymentDevOpsKubernetesGCP
CollaborationProblem-solvingCommunicationCustomer focusTechnical mentorship
Industry SaaS
Job Function Develop and deploy machine learning solutions for customer success
Role Subtype Data Scientist
Tech Domains Python, TensorFlow, Keras, scikit-learn, SQL / PostgreSQL, Kubernetes, Google Cloud Platform
Data ScientistMachine Learning EngineerPythonTensorFlowKerasscikit-learnSQLML pipelinesmodel deploymentDevOpsKubernetesGCPGogle Cloud Platformscalable data pipelinescustomer-facingreinforcement learningBrazeAIpythontensorflowkerassqlkubernetesgcpml pipelinesdevops

Lack of experience with ML libraries, No experience with scalable data pipelines, Inability to work onsite in San Francisco

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