Machine Learning Engineer

Machine Learning Engineer (Data Science & Machine Learning)

You’ll join Calendly’s Data Science & Machine Learning team to build and operate ML-powered features end-to-end, delivering business value from discovery through deployment and monitoring.

Responsibilities

• Own ML-powered features end to end within the ML ecosystem (design → deployment), partnering with Product, Design, Engineering to scope work and define success metrics.
• Execute the full ML lifecycle hands-on:
  • exploratory data analysis, feature engineering, data visualization
  • feature/algorithm selection, experimentation, training & validation
  • model serving, monitoring, and retraining
• Build and implement statistical and ML models to uncover patterns and drive predictions (e.g., forecasting, churn analysis, personalization/recommendations, anomaly detection, NLP).
• Develop and deploy models using managed ML services (e.g., Vertex AI or SageMaker) for high-traffic, low-latency, large-data applications.
• Work with foundation models and the open-source ecosystem, including fine-tuning and prompt engineering.
• Understand and troubleshoot deployment pipelines (build/test/release for ML services and data pipelines).
• Use monitoring/observability tools to triage alerts and incidents, support on-call/incident response, and help prevent recurrence.
• Serve as a subject matter expert for owned features/services (data contracts, SLAs, dependencies).
• Champion adoption of AI tools across the company.

Requirements

• 4+ years of industry experience in applied Machine Learning (or equivalent education/experience).
• Proven track record shipping and operating ML models in production.
• Strong programming and data engineering skills (Python/Scala/Java/SQL).
• Proficiency with ML frameworks (Keras, TensorFlow, PyTorch).

Nice-to-haves

• Experience with managed ML services such as Vertex AI or SageMaker.
• Experience with foundation models, fine-tuning, and prompt engineering.
• Experience across domains like revenue forecasting, churn analysis, personalization/recommendations, anomaly detection, or NLP.