Machine Learning Engineer (Canada)

almost 4 years ago

Toronto, CanadaSenior

Responsibilities

Deploy, execute, validate, monitor, and improve data science solutions.
Create scalable, highly performant machine learning systems.
Build reusable production data pipelines for machine learning models.
Write production-quality code and libraries that can be packaged as containers, installed, and deployed.
Collaborate with data engineers and data scientists to build data and model pipelines and run machine learning tests and experiments.
Manage the infrastructure and data pipelines required to bring machine learning solutions into production.
Troubleshoot production machine learning model issues and recommend retraining, revalidation, and improvements.
Communicate problem context effectively with remote global teams.

Bachelor's degree or higher in computer science or a related field.
5+ years of work experience.
Experience with big data projects involving structured and unstructured data.
Proficiency with Python, Spark, Hadoop, Docker, SQL, statistical tools, relational databases, and cloud environments.
Experience with model evaluation, experimental design, and continuous integration contexts.
Knowledge of machine learning frameworks such as Scikit-learn, TensorFlow, and Keras is preferred.
Knowledge of MLflow, Airflow, Kubernetes, and cloud-native MLaaS offerings such as AWS SageMaker, Azure ML, or Google AI Platform is preferred.
Strong communication, teamwork, collaboration, troubleshooting, and scalable production machine learning skills.

Significant career development opportunities as the company grows.
Small, fast-growing, challenging, and entrepreneurial environment with a high degree of individual responsibility.
Full-time, on-site position in Toronto, Canada.

Apache AirflowApache HadoopApache Spark DockerKerasKubernetesMLflowpytestPythonscikit-learnSQLTensorFlow

5,001-10,000 employees