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 believe that AI can revolutionize the dating industry. Our Data Engineer lead 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 that would be fundamental for our ML engineers 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 data engineer who is passionate about data for AI and values it can bring to our company, Who loves working with data ops at scale; and who is committed to the hard work necessary to continuously improve our ML data pipelines.

In this position, you will be responsible for establishing and executing the strategy for our organization’s ML Data Engine, with an initial focus on agile ML Data OPs. This includes identification of infrastructure components and data stack to be used, design and implementation of pipelines between data systems and teams, automation workflows, data enrichment and monitoring tools all for AI models. As a tech lead specialized in data engineering, you are expected to code and contribute to the stack.

Responsibilities:

• Dive into our dataset and design, implement and scale data pre/post processing pipelines of ML models
• Work on applied ML solutions in the areas of data mining, cleaning, normalizing and modeling
• Be self-motivated in seeking solutions when the correct path isn’t always known
• Collaborate with engineers in conceptualizing, planning and implementing data engineering initiatives working with different stakeholders 
• Design and build data platforms & frameworks for processing high volumes of data, in real time as well as batch, that will be used across engineering teams 
• Build data processing streams for cleaning and modeling text data for LLMs
• Research and evaluate new technologies in the big data space to guide our continuous improvement 
• Collaborate with multi-functional teams to help tune the performance of large data applications 
• Work with Privacy and Security team on data governance, risk and compliance initiatives 
• Work on initiatives to ensure stability, performance and reliability of our data infrastructure 

What we’ll love about you 

• Bachelors in Computer Science, Mathematics, Physics, or a related fields
• 5 years of experience as a data engineer building production-level pre/post-processing data pipelines for ML/DL models, including 2 years of technical leadership experience
• Experience in statistical analysis & visualization on datasets using Pandas or R
• Experience designing and building highly available, distributed systems of data extraction, ingestion, normalization and processing of large data sets in real time as well as batch, that will be used across engineering teams using orchestration frameworks like Airflow, KubeFlow or other pipeline tools
• Demonstrated prior experience in creating data pipelines for text data sets NLP/ large language models
• 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 any public cloud environment - AWS, GCP or Azure
• Significant experience with relational databases and query authoring (SQL) as well as NoSQL databases like DynamoDB etc
• Experience building and maintaining ETL (managing high-quality reliable ETL pipelines)

We’ll really swoon if you have

• 2 years of experience of technical leadership in building data engineering pipelines for AI
• Previous experience in building data pipeline for conversational AI APIs and recommender systems
• Experience with distributed systems and microservices
• Experience with Kubernetes and building Docker images

• Experience with building stream-processing systems, using solutions such as Kafka, Storm or Spark-Streaming
• Strong understanding of applied machine learning topics
• Be familiar with legal compliance (with data management tools) data classification, and retention
• Consistent track record of managing and implementing complex data projects