Machine Learning Engineer

Calendly

20h•Remote

About The Position

Calendly is seeking a Machine Learning Engineer to deliver business value by executing the full machine learning lifecycle hands-on, from problem discovery through model deployment and monitoring. This role reports to the head of Data Science & Machine Learning and is responsible for building and operating ML-powered features that create magical experiences for customers. The Data Science & Machine Learning team drives business insights, strategic decision making, executive level and cross organizational business growth, and magical customer experiences through impactful innovation. They 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. The team has a strong product focus and passion for using machine learning to solve real-world problems, understanding that being an effective MLE is about collaborating with people as much as it is about writing code. The successful candidate will join a high-performing AI team and be an integral part of building new, machine learning-based experiences for internal and external customers.

Requirements

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 skills. Ability to communicate complex technical concepts to both technical and business stakeholders.
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

Responsibilities

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.