Senior Machine Learning Engineer

Qlik is a company focused on data analytics and business intelligence solutions, and they are seeking a Senior Machine Learning Engineer. The role involves designing and optimizing AutoML algorithms, collaborating with cross-functional teams, and deploying machine learning models in production environments.

Responsibilities:

• Design, develop, and optimize AutoML algorithms and frameworks to automate model selection, hyperparameter tuning, feature engineering, and deployment workflows
• Research, prototype, and integrate state-of-the-art machine learning and deep learning techniques into the AutoML framework
• Collaborate cross-functionally with data scientists, software engineers, MLOps engineers, and domain experts to understand user requirements and translate them into scalable solutions
• Build, test, and deploy machine learning models in production environments, ensuring robustness, scalability, and efficiency
• Contribute to the design of end-to-end machine learning pipelines, including data ingestion, preprocessing, model training, evaluation, deployment, and monitoring
• Implement model monitoring, drift detection, and retraining strategies to ensure sustained model performance over time
• Optimize model performance, resource utilization, and inference speed for production use cases
• Ensure adherence to best practices in software engineering, including code reviews, version control, testing, and documentation
• Stay up to date with industry trends, academic research, and emerging technologies in machine learning, AutoML, and AI infrastructure
• Mentor and provide technical guidance to junior team members

Requirements:

• Master's degree in Electrical Engineering, Computer Science, Machine Learning, Data Science, or a related field
• 2 years of experience (before/during/after degree) as Test Engineer, Machine Learning Engineer, Software Engineer or related
• 6 months (may be before/during/after degree and concurrent with 2 years) in the following: Python and one additional language, such as Java, C++, Go, or comparable
• Machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost or comparable
• Fine-tuning large language models (GPT-3, Jurassic-Jumbo, GPT-J) on GCP and Google Colab and deploying them into production for tasks such as text completion and summarization
• Building production-quality machine learning software using BERT or comparable LLM
• Statistical and mathematical concepts related to machine learning
• Working with cloud platforms and distributed computing technologies such as AWS and Google Cloud