Machine Learning Engineer

Overview

Ready to make a serious impact? Millions of people already rely on Calendly, and we’re still in the midst of exciting product growth — it’s a fantastic time to join us. Everything you’ll work on here will accelerate your career to the next level. If you want to learn, grow, and do the best work of your life alongside the best people you’ve ever worked with, then we hope you’ll consider allowing Calendly to be a part of your professional journey.

About the team & opportunity

What’s so great about working on Calendly’s Data Science & Machine Learning team?

We make things possible for our customers through innovation in data, analytics and AI.

Why do we need you? We are looking for a Machine Learning Engineer who will deliver business value by executing the full machine learning lifecycle hands-on, from problem discovery through model deployment and monitoring. You will report to the head of Data Science & Machine Learning and will be responsible for building and operating ML-powered features that create magical experiences for our customers.

Our team:

• Drives business insights, strategic decision making, executive level and cross organizational business growth, and magical customer experiences for our end customers through impactful innovation.
• Works closely with product, design, marketing, customer success, and engineering teams to implement ML models that improve the customer journey in service to growth and efficiency (for example, understanding the relationships among customers’ behavior and business performance).
• Has a strong product focus and passion for using machine learning to solve real world problems, and understands that being an effective MLE is about collaborating with people as much as it is about writing code.

You will join a high performing AI team and be an integral part of building new, machine learning based experiences for internal and external customers alike.

What you’ll do

On a typical day, you’ll own features end to end within our ML ecosystem, with growing independence and impact.

• Own ML powered features from design through deployment, partnering with product, design, and engineering to scope work and define success metrics.
• Understand and share domain knowledge, answering domain specific questions for your product area and documenting what you learn for the team.
• Prioritize your work independently, balancing feature development, quality, and maintenance, and communicating tradeoffs clearly.
• Proactively seek and offer support to teammates pairing, reviewing, and collaborating to move projects forward.
• Understand and troubleshoot our deployment pipelines, including build, test, and release steps for ML services and data pipelines.
• Use our monitoring and observability tools to effectively triage alerts and incidents, collaborating with partners to restore service and prevent recurrence, and participate in the team’s on-call rotation and incident response.
• Serve as a subject matter expert for the features and services you own, including their data contracts, SLAs, and dependencies.
• Be a frequent user of AI Tools and champion of adoption to the rest of the company.

What do we need from you?

• 4+ years of industry experience in applied Machine Learning or closely related fields (or equivalent combination of education and experience) with a demonstrated track record of shipping and operating ML models in production.
• Deep and demonstrated ability to traverse the full spectrum of ML life cycle: exploratory data analysis, feature engineering, data visualization, feature and algorithm selection, model experimentation, model training and validation, model serving, monitoring and retraining
• Experience developing and implementing statistical and ML models to uncover patterns, trends, and predictions in areas such as revenue forecasting, churn analysis, personalization and recommendation, anomaly detection, or natural language processing.
• Hands-on experience implementing ML models using a managed service (for example, Vertex AI or Sage Maker) for high-traffic, low-latency, large-data applications that produced tangible impact for end users.
• Understanding of foundation models and the open-source…