KinderCare Education, LLC

Senior Machine Learning Engineer- Fully Remote!

Vaga remota de Senior Machine Learning Engineer com fit claro de localização do candidato.

Publicada11 de jul. de 2026

Países elegíveis10 países aceitos

Sinal de senioridadeSenior

Modelo de trabalhoRemoto

Locais aceitos para candidatos

AustráliaBrasilCanadáFrançaAlemanhaÍndia+4 mais

Azure CI/CD LLM Python Spark SQL

Resumo da vaga

Senior Machine Learning Engineer- Fully Remote!

Requisitos e responsabilidades

Conteúdo da vaga extraído em seções para revisão mais rápida.

Responsibilities:

Databricks-Native ML Development: Design, develop, and deploy machine learning solutions using Databricks technologies including PySpark, Spark SQL, MLflow, Feature Store, AutoML, and notebooks to standardize experimentation and feature reuse.
End-to-End ML Pipeline Architecture: Build scalable ML pipelines across the full lifecycle—from data ingestion and feature engineering to model validation, deployment, monitoring, and retraining within the Lakehouse platform.
MLOps & Model Lifecycle Management: Implement CI/CD, model versioning, governance, automated retraining, and production deployment using MLflow Model Registry, Databricks Workflows, and Model Serving.
Advanced Databricks Capabilities: Leverage AutoML, Mosaic AI components, vector search, and Model Serving to accelerate experimentation and enterprise AI adoption while maintaining governance and scalability.
Applied Data Science & Mentorship: Perform exploratory analysis and apply statistical and machine learning techniques including regression, classification, and clustering. Mentor junior developers and analytics professionals on ML guidelines and operationalization.
Cross-Functional Collaboration: Partner with Data Engineering, Analytics, Product, and business collaborators to align AI solutions with enterprise architecture, governance, and business objectives.
Performance, Governance & Reliability: Optimize Spark performance and cost efficiency while implementing monitoring, alerting, lineage tracking, and access controls through Unity Catalog and related governance frameworks.
Platform Enablement & Scalability: Develop reusable frameworks, templates, and standards that accelerate scalable, governed ML adoption across the organization.

Qualifications:

Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, or a related quantitative field (or equivalent experience). Master’s degree or higher in a related field preferred.
4+ years of experience in Machine Learning Engineering or Data Engineering, with significant hands-on expertise in Databricks technologies including Delta Lake, MLflow, Feature Store, and Unity Catalog.
Success in delivering production-grade ML pipelines end-to-end, from data ingestion and feature engineering through deployment, monitoring, and continuous improvement.
Experience using AI-assisted development tools such as Cursor, Claude, or GitHub Copilot to accelerate development, testing, and optimization of distributed ML workloads.
Strong proficiency in Python, PySpark, and Spark SQL, with deep knowledge of distributed computing, Spark optimization, and scalable ML architecture.
Experience designing Databricks-native ML solutions employing platform capabilities such as MLflow, AutoML, Feature Store, Delta Lake, and Model Serving.
Familiarity with CI/CD and DevOps tooling including GitHub Actions, Azure DevOps, or GitLab CI.
Hands-on experience building and evaluating ML models using frameworks such as scikit-learn, XGBoost, or LightGBM.
Solid grasp of feature engineering, experiment tracking, model validation, and performance evaluation. Experience with RAG architectures, vector databases, embedding pipelines, and LLM-based applications is a plus.
Ability to mentor engineers and data scientists, lead technical discussions, and influence ML engineering methodologies across teams.
Experience building reusable ML frameworks and modernizing legacy workflows into scalable, governed Databricks-native pipelines.

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