Machine Learning Engineer

ABOUT THE ROLE We are seeking a highly skilled Machine Learning Engineer to join our Data Science & Machine Learning team at Calendly. As a Machine Learning Engineer, you will be responsible for delivering business value by executing the full machine learning lifecycle, from problem discovery through model deployment and monitoring. You will work 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. WHAT YOU'LL DO As a Machine Learning Engineer at Calendly, you will own features end to end within our ML ecosystem, with growing independence and impact. Your responsibilities will include: - Owning ML powered features from design through deployment, partnering with product, design, and engineering to scope work and define success metrics - Understanding and sharing domain knowledge, answering domain specific questions for your product area and documenting what you learn for the team - Prioritizing your work independently, balancing feature development, quality, and maintenance, and communicating tradeoffs clearly - Proactively seeking and offering support to teammates, pairing, reviewing, and collaborating to move projects forward - Understanding and troubleshooting our deployment pipelines, including build, test, and release steps for ML services and data pipelines - Using our monitoring and observability tools to effectively triage alerts and incidents, collaborating with partners to restore service and prevent recurrence, and participating in the team’s on-call rotation and incident response - Serving as a subject matter expert for the features and services you own, including their data contracts, SLAs, and dependencies - Being a frequent user of AI Tools and champion of adoption to the rest of the company WHAT YOU'LL NEED To be successful in this role, you will need: - 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 SageMaker) for high-traffic, low-latency, large-data applications that produced tangible impact for end users - Understanding of foundation models and the open-source ecosystem, including model fine-tuning and prompt engineering for real product use cases - Strong programming (Python / Scala / Java / SQL etc) and data engineering skills - Proficiency in ML frameworks such as: Keras, Tensorflow and PyTorch and ETL and ML workflow frameworks like Apache Spark, Beam, Airflow and VertexAI - Experience working with time series data and related machine learning problems. Working knowledge of semantic search and embeddings - Recognize when to seek assistance and willing to learn whatever is needed to get the job done; curiosity and growth mindset are essential - Strong verbal and written communication