Machine Learning Engineer

Stack

Responsibilities

• ▹Own ML-powered features end to end, partnering with product, design, and engineering
• ▹Understand and document domain knowledge for your product area
• ▹Prioritize work independently across feature development, quality, and maintenance
• ▹Support teammates through pairing, reviewing, and collaboration
• ▹Troubleshoot deployment pipelines for ML services and data pipelines
• ▹Use monitoring and observability tools to triage alerts and participate in on-call rotation
• ▹Serve as subject matter expert for owned features, data contracts, SLAs and dependencies
• ▹Be a frequent user and champion of AI tool adoption company-wide

Requirements

• ▹4+ years of industry experience in applied Machine Learning with a track record of shipping and operating ML models in production
• ▹Deep ability across the full ML lifecycle: EDA, feature engineering, visualization, model experimentation, training, validation, serving, monitoring
• ▹Experience developing statistical/ML models (revenue forecasting, churn analysis, personalization, anomaly detection, NLP)
• ▹Hands-on experience with managed services (Vertex AI, SageMaker) for high-traffic, low-latency applications
• ▹Understanding of foundation models, open-source ecosystem, fine-tuning and prompt engineering
• ▹Strong programming (Python/Scala/Java/SQL) and data engineering skills
• ▹Proficiency in ML frameworks (Keras, TensorFlow, PyTorch) and ETL/workflow tools (Spark, Beam, Airflow, VertexAI)
• ▹Experience with time series data
• ▹Working knowledge of semantic search and embeddings
• ▹Authorized to work in the US

Soft skills

What we offer

• ▹Competitive base salary (tiered by location)
• ▹Top Performer Bonus program
• ▹Equity awards
• ▹Competitive benefits package
• ▹Remote work

About the company

Calendly's Data Science & Machine Learning team drives business insight and customer experience innovation through ML models.