Compensation Overview

This is a hybrid role based in our Palo Alto or San Francisco offices and will require you to be in office Tuesdays and Thursdays.

What’s so interesting about this role?

We at Grindr believe that AI can revolutionize the dating industry. Our Machine Learning Engineer is responsible for building high quality ML datasets at scale, used to train ML models that power AI-centric features. In this pivotal role, you will have the opportunity to build foundational tools and data pipelines to ingest, normalize and clean the valuable data and features that would be fundamental for our ML engineers in Grindr to build AI tools including recommendations, LLMs, ads, visual search, growth/notifications, trust and safety. 

What’s the job?

We’re looking for an exceptional Machine Learning Engineer who is passionate about AI and values it can bring to Grindr, Who loves building Models at scale; and who is committed to the hard work necessary to continuously improve our AI products. 

In this position, you will be responsible for establishing and executing the strategy for our organization’s Machine Learning products. 

Responsibilities:

• 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)

What we’ll love about you 

• 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

We’ll really swoon if you have    

• Good understanding of classical ML models
• Experience with vector databases (store , retrieve , rank) for LLM, RecSys, etc. 
• Experience with NLP-text-data preprocessing and labeling
• Experience image dataset management, transformation and labeling for computer vision. Experience in recommendation systems Experience with Kubernetes and building Docker images

• Strong understanding of applied machine learning and deep learning topics
• Be familiar with legal compliance (with data management tools) data classification, and retention
• Consistent track record of implementing end-to-end machine learning models from data cleaning to model deployment