Python Machine Learning Engineer

About this opportunity

Turing is seeking an experienced Machine Learning Developer to lead end-to-end development of advanced ML solutions, from data pipelines and model architecture to deployment and monitoring. The role combines hands-on engineering with technical leadership, cross-functional collaboration, model optimization, and application of current AI and machine learning research.

What the listing describes

• ✓Own end-to-end data science and machine learning solution development, from data pipelines and model design to deployment and monitoring
• ✓Translate business objectives into robust machine learning architectures that capture business logic and context
• ✓Collaborate with Product, Engineering, and Business stakeholders to define problem statements and success metrics
• ✓Evaluate and optimize models for performance, scalability, and accuracy using state-of-the-art techniques
• ✓Stay current with advancements in AI and machine learning research and apply relevant innovations

Requirements shown on the source

• ✓Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, or a related quantitative field
• ✓At least 4 years of hands-on data science or machine learning development experience
• ✓Proficiency in supervised and unsupervised learning
• ✓Experience with time-series forecasting
• ✓Knowledge of Natural Language Processing
• ✓Knowledge of Computer Vision
• ✓Strong understanding of statistical modeling and inference
• ✓Expertise in Python and core libraries including Pandas, NumPy, and Scikit-learn
• ✓Ability to understand and apply different machine learning models to real-world use cases
• ✓Strong understanding of data preprocessing, feature engineering, model tuning, and evaluation metrics
• ✓Proven ability to design scalable, production-grade machine learning systems
• ✓Available for at least 4 hours per day and a minimum of 20 hours per week
• ✓Able to provide 4 hours of overlap with PST working hours

Relevant backgrounds mentioned

• ✓Expertise in deep learning, including convolutional neural networks, recurrent neural networks, and transformers
• ✓Experience with cloud data platforms such as Databricks and AWS
• ✓Hands-on experience with PySpark
• ✓Hands-on experience with the Databricks platform
• ✓Experience with Kaggle competitions and machine learning benchmarks such as MLEBench
• ✓Experience experimenting with technologies and frameworks based on research papers from leading conferences and journals