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Machine Learning Engineer

Calendly
AI / ML Engineer • Remote • Teljes munkaidő • 📍 Remote (US)
📄 Eredeti hirdetés megtekintése (EN)

Calendly is seeking a Machine Learning Engineer for its Data Science & Machine Learning team who executes the full ML lifecycle hands-on, from problem discovery through model deployment and monitoring. Reporting to the head of Data Science & Machine Learning, you will build and operate ML-powered features that create magical customer experiences. The team works closely with product, design, marketing, customer success, and engineering. Remote (US).

Stack

AirflowApacheConfluenceJavaKerasPythonPyTorchScalaSparkSQLTensorFlow

Feladatok

  • ▹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
  • ▹Prioritize your work independently, balancing feature development, quality, and maintenance, and communicating tradeoffs clearly
  • ▹Proactively seek and offer support to teammates through pairing, reviewing, and collaborating
  • ▹Understand and troubleshoot deployment pipelines, including build, test, and release steps for ML services and data pipelines
  • ▹Use monitoring and observability tools to triage alerts and incidents, collaborate to restore service, and participate in on-call rotation and incident response
  • ▹Serve as 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 a champion of adoption across the company

Elvárások

  • ▹4+ years of industry experience in applied Machine Learning or closely related fields (or equivalent education and experience), with a track record of shipping and operating ML models in production
  • ▹Deep, demonstrated ability across the full ML lifecycle: EDA, feature engineering, data visualization, feature and algorithm selection, experimentation, training and validation, serving, monitoring, and retraining
  • ▹Experience developing statistical and ML models for areas such as revenue forecasting, churn analysis, personalization and recommendation, anomaly detection, or NLP
  • ▹Hands-on experience implementing ML models using a managed service (e.g., Vertex AI or SageMaker) for high-traffic, low-latency, large-data applications
  • ▹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/ML workflow frameworks like Apache Spark, Beam, Airflow, and VertexAI
  • ▹Experience with time series data and related ML problems; working knowledge of semantic search and embeddings
  • ▹Recognizes when to seek assistance and is willing to learn whatever is needed; curiosity and growth mindset
  • ▹Strong verbal and written communication; ability to communicate complex technical concepts to technical and business stakeholders; comfortable working remotely with tools like Slack and Confluence
  • ▹Authorized to work lawfully in the USA (Calendly does not provide immigration sponsorship)

Soft skillek

Strong product focus and collaboration skillsCuriosity and growth mindsetIndependent prioritization and clear communication of tradeoffsCommunication to technical and business audiences

Amit kínálunk

  • ▹Tier 1 (San Francisco, San Jose, New York City) base salary range: USD 202,542 - 245,434
  • ▹Tier 2 (Chicago, Austin, Denver, Boston, Washington D.C., Philadelphia, Portland, Seattle, Miami, and all other CA cities) range: USD 185,664 - 224,981
  • ▹Tier 3 (all other locations) range: USD 168,785 - 204,528
  • ▹Top Performer Bonus program (or sales incentive), equity awards, and competitive benefits for all full-time (30 hrs/week) employees
  • ▹Role may require occasional travel for company events, team collaboration, or offsites