Machine Learning Engineer
August 8
🏢 In-office - Los Angeles
Description
• Work closely with the Engineering team to uncover opportunities for machine learning automation and predictive modeling across all Machina Labs offerings. • Perform data mining to find interesting and impactful data and finally develop, train and deploy models. • Define proper metrics for each project that can tie into the business objective • Perform data cleaning and ETLs to extract relevant features for modeling • Help other members of the team with data analysis and proper interpretation of data • Proper A/B testing of different solutions or models • Build pipeline that supports running multiple machine learning models in parallel in production. • Build monitoring tools to understand the data quality and performance of complex systems. • Empathetically help other engineers grow • Actively participate in the interview process
Requirements
• Bachelor’s, MS or Ph.D. in related fields (Data Science, Computer Science and Machine Learning, Statistics or a quantitative-related field) or equivalent professional experience. • 2+ years of experience with machine learning systems, algorithms or applications such as deep learning and time series analysis. • Startup / early product development experience. • Good understanding of fundamental CS algorithms and their scaling behaviors in data structures, algorithms, and software design • Strong programming background, with extensive experience in Python • In-depth knowledge of build/release systems and process • Experience working with data warehouses, data lakes, and ETL • Experience working with big data platforms (Hadoop, Spark, Hive) and orchestration frameworks (Airflow) and analytic environments (Databricks, Sagemaker, Jupyter) • Able to quickly learn new technologies • Experience in fast-paced iterative design and manufacturing environments • Strong communicator who can explain complex topics to both a technical and non-technical audience • Experience solving complex problems with little to no supervision on schedule as an individual or as a member of an integrated team
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