Machine Learning Engineer

Role: Machine Learning Engineer (MLE)

Join Calendly’s Data Science & Machine Learning team to deliver business value by owning the full ML lifecycle—end to end—from problem discovery through model deployment and ongoing monitoring.

Responsibilities

• Own ML-powered features end to end within the ML ecosystem, from design through deployment.
• Partner with Product, Design, and Engineering to scope work and define success metrics.
• Apply domain expertise to answer product-area questions and document learnings for the team.
• Balance feature development, quality, and maintenance while communicating tradeoffs.
• Support teammates through pairing, reviews, and collaboration.
• Understand and troubleshoot ML deployment pipelines (build, test, release) and data pipelines.
• Use monitoring and observability tools to triage alerts/incidents, collaborate on recovery, and help prevent recurrence; participate in on-call/incident response.
• Act as a subject matter expert for owned services/features, including data contracts, SLAs, and dependencies.
• Use AI tools frequently and champion adoption across the company.

Requirements

• 4+ years of industry experience in applied Machine Learning (or equivalent education/experience).
• Demonstrated track record shipping and operating ML models in production.
• Strong ability across the full ML lifecycle, including:
  • exploratory data analysis (EDA)
  • feature engineering and data visualization
  • feature/algorithm selection
  • experimentation, training, and validation
  • model serving
  • monitoring and retraining
• Hands-on experience building statistical and ML models for use cases such as:
  • revenue forecasting, churn analysis
  • personalization/recommendation
  • anomaly detection
  • natural language processing (NLP)
• Hands-on experience implementing ML models (continues in posting but is cut off in provided text).