Staff 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 Staff 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 Staff Machine Learning Engineer at Calendly

On a typical day, you will:

• Collaborate cross-functionally with software engineers, product managers, and analysts to understand business needs, define priorities, and drive impactful machine learning solutions
• Design, develop, and deploy ML models and pipelines at scale, supporting both batch and real-time use cases; architect ML and backend solutions as needed
• Leverage cloud-based ML services and tools to build reusable, high-performance systems that enable rapid model development, low-latency inference, and seamless model maintenance
• Optimize ML models for scale and efficiency, ensuring they meet latency SLAs while handling Calendly’s production traffic; conduct live experiments to validate and refine model performance.

What do we need from you?

• Deep understanding of modern LLMs and Generative AI – Strong familiarity with recent advancements in large language models, their applications, fine-tuning techniques, retrieval-augmented generation (RAG), multimodal models, and agentic frameworks
• Strong foundation in machine learning and statistics – Proficiency in probabilistic modeling, statistical inference, hypothesis testing, and traditional ML techniques
• Solid software engineering fundamentals – Extensive experience with Python, CI/CD for ML workflows, containerization (Docker, Kubernetes), and monitoring/observability of deployed models
• Backend engineering and ML infrastructure – Proven experience in building scalable APIs, integrating ML models into production systems, and deploying on cloud platforms (AWS, GCP, Azure); familiarity with distributed computing and database technologies is a plus
• Hands-on with modern ML tooling – Practical experience with PyTorch, TensorFlow, Hugging Face, LangChain, vector databases, and model-serving frameworks
• Self-motivated and resourceful – You take initiative, find creative solutions, and don’t let roadblocks stop you from making progress
• Bias for action and results-oriented – You thrive in ambiguity, move fast, and focus on delivering impact rather than just building technology; you recognize when to seek assistance and are willing to learn whatever is needed to get the job done
• Collaborative & communicative – You can work cross-functionally with engineering, product, analytics and business teams to align on goals and deliver solutions that scale
• Curiosity and continuous learning – You stay on top of the latest ML/AI advancements and proactively seek ways to integrate them into real-world applications
• 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 recruiting@calendly.com . 

Calendly is registered as an employer in many, but not all, states. If you are located in Alaska, Alabama, Delaware, Hawaii, Idaho, 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.

All candidates can find our Candidate Privacy Statement here

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