Staff Machine Learning Engineer, Grindr

• Bachelors in Computer Science, Mathematics, Physics, or a related fields
• 5+ years of experience as a Machine Learning engineer building production-level pre/post-processing data pipelines for ML/DL modelExperience in statistical analysis & visualization on datasets using SQL, Pandas or R
• Demonstrated prior experience in creating data cleaning pipelines for text data sets NLP/ large language models
• Transform research prototypes into high-quality production code, ensuring systems are maintainable, scalable and performant
• Ability to produce well-engineered software, including appropriate automated test suites, technical documentation, and operational strategy
• Excellent coding skills in Python, Java, bash, SQL, and expertise with Git version control
• Experience using big data technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, pySpark)
• Experience with building machine/deep learning models with at least one common framework such as PyTorch, Tensorflow, or Keras
• Experience with any public cloud environment - AWS, GCP or Azure

Desirable

• In this position, you will be responsible for establishing and executing the strategy for our organization’s Machine Learning products
• Feature engineering at scale. This includes handling missing values, removing duplicates, and transforming data into a suitable format for analysis and model training. For recommendation systems, it might also involve normalizing user ratings, encoding categorical data, or processing textual and image data with NLP and computer vision techniques
• Feature Store Implementation: Building or integrating a feature store to manage, share, and reuse features across ML projects, ensuring consistency and reducing redundancy in feature engineering efforts. This project would involve understanding the needs of ML models and creating a centralized repository for features
• Model Training and Evaluation Support: providing support for model training and evaluation, including setting up data splits for training, validation, and testing, and ensuring that data is correctly partitioned and accessible to the ML models. They might also work on optimizing data processing for GPU-based training environments
• Requiring a blend of skills in software engineering, data management, and an understanding of ML workflows
• Exploratory/statistical data analysis (EDA)