Senior Machine Learning Engineer

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? Well, we are looking for a Senior Machine Learning Engineer who will bring the track record of delivering business value through executing hands-on full machine learning lifecycle. You will report to the head of Data Science & Machine Learning and will be responsible for driving new initiatives using the latest advancements in ML, working closely with cross-functional teams, and helping to drive business insights & growth as well as creating magical experiences for our end customers through innovation. We have a product focus and passion for using machine learning to solve real-world problems, and understand that being an effective MLE is about collaborating with people as much as it is about writing code. You will join a great data team and be an integral part of building new, machine learning-based experiences for internal and external customers alike.

A day in the life of a Senior Machine Learning Engineer at Calendly

On a typical day, you will be working on:

• Working with unique, large-structured time series data sets to build and continuously improve innovative machine learning models for Calendly product use cases
• Working collaboratively with partners including software engineering, product managers, decision and data scientists, to impact the business by understanding and prioritizing requirements for machine learning models
• Hands-on developing, "productionizing," and operating machine learning models and pipelines at scale, including both batch and real-time use cases 
• Leveraging machine learning cloud services and tools to develop reusable, highly differentiating and high-performing machine learning systems, enable fast model development, low-latency serving and ease of model quality upkeep
• Optimizing ML models to meet latency SLAs at the scale of Calendly's production traffic and launch live experiments to evaluate model performance

What do we need from you?

• 5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields 
• Deep and demonstrated ability to traverse the full spectrum of ML life cycle: EDA, feature engineering, data visualization, feature and algorithm selection, model experimentation, model training and validation, model serving, monitoring and re-training
• Develop and implement advanced statistical and ML models to uncover patterns, trends, and predictions (Ex.: revenue forecasting, churn analysis, personalization and recommendation, anomaly detection, natural language processing)
• Consistent record of efficiently implementing ML models using a managed service (VertexAI / Sagemaker) for high traffic, low latency, large data applications that produced substantial impact on the end users
• Deep understanding of the the foundation models, open source ecosystem, model fine-tuning, prompt engineering etc.
• Strong programming (Python / Scala / Java / SQL etc) and data engineering skills
• Proficiency in ML frameworks such as: Keras, Tensorflow and PyTorch (in that order of importance) and ETL and ML workflow frameworks like Apache Spark, Beam, Airflow and VertexAI
• Deep experience working with time series data and related machine learning problems; working knowledge of semantic search and embeddings 
• Familiarity with Retrieval-Augmented Generation techniques to improve content quality, orchestration framework such as Langhcain or Microsoft Semantic kernel
• The ability to recognize when to seek assistance and willing to learn whatever is needed to get the job done; ideally, you have some research experience
• You have strong verbal and written communication skills and the ability to communicate complex technical concepts to both technical and business stakeholders
• You are comfortable working remotely and with enabling tools like Slack, Confluence, etc.
• Authorized to work lawfully in the United States of America as Calendly does not engage in immigration sponsorship at this time 

What’s in it for you? 

Ready to make a serious impact? Millions of people already rely on Calendly’s products, and we’re still in the midst of our growth curve — 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. 

If you are an individual with a disability and would like to request a reasonable accommodation as part of the application or recruiting process, please contact us at [email protected] . 

Calendly is registered as an employer in many, but not all, states. If you are located in Alaska, Hawaii, Montana, North Dakota, South Dakota, Nebraska, Iowa, West Virginia, and Rhode Island, you will not be eligible for employment. Note that all individual roles will specify location eligibility.

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Candidates residing in California may visit our Notice at Collection for California Candidates here: Notice at Collection

The ranges listed below are the expected annual base salary for this role, subject to change.
Calendly takes a number of factors into consideration when determining an employee’s starting salary, including relevant experience, relevant skills sets, interview performance, location/metropolitan area, and internal pay equity.
Base salary is just one component of Calendly’s total rewards package. All full-time (30 hours/week) employees are also eligible for our Quarterly Corporate Bonus program (or Sales incentive), equity awards, and competitive benefits.
Calendly uses the zip code of an employee’s remote work location, or the onsite building location if hybrid, to determine which metropolitan pay range we use. Current geographic zones are as follows:

• Tier 1: San Francisco, CA, San Jose, CA, New York City, NY
• Tier 2: Chicago, IL, Austin, TX, Denver, CO, Boston, MA, Washington D.C., Philadelphia, PA, Portland, OR, Seattle, WA, Miami, FL, and all other cities in CA.
• Tier 3: All other locations not in Tier 1 or Tier 2