Machine Learning Engineer

Summary

Join Sourcescrub's globally-distributed Data Engineering team as an experienced Applied Machine Learning Engineer. Leveraging your Python expertise, you'll build scalable ML solutions for high-impact projects using cutting-edge technologies, including LLMs. You'll work with large datasets, developing advanced applications and owning the implementation of new ML services. This full-time, contract role offers significant growth opportunities and involves collaborating with cross-functional teams. The position reports to the Director of Engineering, Data, and requires proven experience in designing, implementing, and scaling ML solutions.

Requirements

• 3+ years of proven experience as an Applied ML Engineer or similar role, with a focus on Python
• 3+ years of advanced Python skills, with strong experience in libraries such as Pandas, NumPy, TensorFlow, PyTorch, and Scikit-Learn
• Demonstrated experience in deploying and optimizing ML models at scale, with knowledge of LLMs and other scalable architectures
• 3+ years of strong knowledge of SQL and database management for data storage and retrieval
• 2+ years of experience with version control, particularly Git
• Strong analytical, problem-solving, and attention to detail skills
• Excellent communication and teamwork, particularly in agile environments

Responsibilities

• Develop and maintain ML models with a focus on scalability, especially for deploying and optimizing LLMs in production environments
• Collaborate with cross-functional teams to deliver end-to-end ML solutions, from data preprocessing to model deployment
• Implement data preprocessing, feature engineering, and model evaluation processes to ensure the highest standards of accuracy and efficiency
• Develop and manage ML pipelines in Python for performance at scale
• Leverage cloud platforms (e.g., Azure, AWS) for deploying, scaling, and monitoring models in production
• Stay current with advancements in LLMs, ML frameworks, and Python development best practices

Preferred Qualifications

• Experience with web scraping frameworks such as Scrapy and BeautifulSoup for data extraction and preprocessing
• Familiarity with ML Ops tools and practices for streamlined model lifecycle management
• Experience with data engineering tools to support robust ML pipelines
• Background in NLP, computer vision, or time-series forecasting, especially with large-scale data