Lmts Machine Learning Engineer

swisssalesjobs.ch

Base: $172,500 - $260,100 annually; bonus/equity: ...
Python software engineering proficiency
Machine learning tools and infrastructure
Scalable data pipelines design
We are seeking a highly skilled Machine Learning Engineer to design, build, and productionalize models that drive customer growth, engagement and retention

Job Summary

  • We are seeking a highly skilled Machine Learning Engineer to design, build, and productionalize models that drive customer growth, engagement and retention.
  • This role focuses on attrition prediction and mitigation by identifying customers at risk of churn and surfacing proactive interventions to improve customer satisfaction and lifetime value.
  • Salesforce offers benefits and resources to support work-life balance and accelerate impact through AI agents, enabling employees to bring the power of Agentforce to organizations of all sizes.

Matching Summary

We are seeking a highly skilled Machine Learning Engineer to design, build, and productionalize models that drive customer growth, engagement and retention.

Salary

Base: $172,500 - $260,100 annually; Bonus/Equity: Not specified; Benefits: Time off, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), employee stock purchasing program

Skills & Requirements

Must-have

  • Python software engineering proficiency
  • Machine learning tools and infrastructure
  • Scalable data pipelines design
  • Model evaluation and drift monitoring
  • Containerization and orchestration technologies
  • Feature engineering on big data
  • ML lifecycle management tools

Nice-to-have

  • Mentoring junior engineers
  • Agile development methodology
  • Test-Driven Development
  • Experience with AI Agents
  • Next best action recommendation systems
  • Shared ML frameworks contribution
  • Feature Stores familiarity

Key Requirements

  • Experience taking models from research to production
  • Proficiency in Python and SQL
  • Familiarity with ML libraries like scikit-learn, XGBoost, Pytorch, TensorFlow
  • Experience with containerization (Docker) and orchestration (Kubernetes)
  • Experience with ML lifecycle tools such as ML Flow, Airflow, Kubeflow
  • Experience owning and operating services throughout software lifecycle
  • Experience communicating technical vision and mentoring
  • Experience developing AI Agents integrating predictive and generative workflows

Work Rights

Not specified

Tailored Resume

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