Machine Learning Engineer

Role overview Develop machine learning models from design through production deployment, using practices that make model workflows repeatable and maintainable. The role also involves exploring current research and bringing promising approaches into practical solutions with cross-functional teams.

Responsibilities - Design, train, and deploy machine learning models for production use. - Apply MLOps practices to streamline model development and delivery. - Collaborate with colleagues across disciplines to develop and improve solutions. - Review emerging research and explore new approaches for relevant problems.

Requirements - A degree in computer science or engineering, or equivalent practical machine learning experience. - Fluent written and spoken English. - Strong Python skills and experience with TensorFlow or PyTorch. - Experience with Docker and Kubernetes. - Familiarity with ML orchestration tools such as Kubeflow, MLflow, or Airflow, plus feature stores and large-scale data tools such as Spark or Kafka. - Strong problem-solving, research analysis, and communication skills.

Benefits and work setup - Remote work with flexible hours. - Opportunities for professional growth and career development. - Supportive team environment and wellness perks.