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Calendly

Machine Learning Engineer

Remote - US (Remote)From $245.4kmidAdded 1 month ago

About this role

Calendly seeks a Machine Learning Engineer to design, build, and operate ML-powered features across the full lifecycle—from problem discovery to production deployment and monitoring. You'll collaborate with cross-functional teams to deliver AI innovations that enhance customer experiences and drive business growth.

What you'll do

  • Own ML-powered features end-to-end, from design through deployment and serving in production
  • Collaborate with product, design, and engineering teams to scope work and define success metrics
  • Execute the complete ML lifecycle including data analysis, feature engineering, model training, validation, and monitoring
  • Troubleshoot deployment pipelines and participate in incident response and on-call rotation
  • Document domain knowledge and serve as subject matter expert for owned services and data contracts
  • Champion adoption of AI tools and best practices across the organization

What they're looking for

  • Python, Scala, Java, or SQL programming
  • ML frameworks (TensorFlow, Keras, PyTorch)
  • ML workflow tools (Apache Spark, Beam, Airflow, Vertex AI)
  • Time series analysis and machine learning
  • Foundation models, fine-tuning, and prompt engineering
  • Statistical modeling and data visualization
  • Managed ML services (Vertex AI, SageMaker)
  • Communication of technical concepts to diverse stakeholders

Benefits

  • Remote work opportunity (US-based)
  • Opportunity to work on products used by millions
  • Career acceleration and professional growth
  • Collaboration with high-performing AI team
  • Strong product focus environment
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Calendly

Calendly builds scheduling and calendar software enhanced with machine learning capabilities. The company is hiring Machine Learning Engineers to design and deploy AI-powered features that improve customer experiences across its platform.

Website
calendly.com
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Likely interview questions

  • Walk us through a machine learning model you deployed to production. How did you handle monitoring, retraining, and what metrics did you use to measure success?
  • Describe your experience with the full ML lifecycle—from problem scoping through deployment. Which stage do you find most challenging and why?
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