What you will be doing:

• Develop ML models supporting ranking, retrieval, and generative AI use-cases.

• Brainstorm with Product Managers, Designers and Frontend Engineers to conceptualize and build new features for our large (and growing!) user base.

• Produce high-quality results by leading or contributing heavily to large multi-functional projects that have a significant impact on the business.

• Actively own features or systems and define their long-term health, while also improving the health of surrounding systems.

• Support in the development of sustainable data collection pipelines and management of ML features.

• Assist our skilled support team and operations team in triaging and resolving production issues.

• Mentor other engineers and deeply review code.

• Improve engineering standards, tooling, and processes.

What you should have:

• 7+ years of applicable engineering experience.

• Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala or Java.

• Built with common ML frameworks like pytorch, Tensorflow, Keras, XGBoost, or Scikit-learn

• Experience building batch data processing pipelines with tools like Apache Spark, Hadoop, EMR, Map Reduce, Airflow, Dagster, or Luigi.

• Worked on generative AI apps with Large Language Models and possibly fine tuned them

• An analytical and data driven mindset, and know how to measure success with complicated ML/AI products.

• Put machine learning models or other data-derived artifacts into production at scale.

• Experience leading technical architecture discussions and helped drive technical decisions within the team.

• The ability to write understandable, testable code with an eye towards maintainability.

• Strong communication skills and you are capable of explaining complex technical concepts to designers, support, and other specialists.

• Strong computer science fundamentals: data structures, algorithms, programming languages, distributed systems, and information retrieval.

Nice to have:

• Expertise in retrieval systems and search algorithms.

• Familiarity with vector databases and embeddings.

• Knowledge of using multiple data types in RAG solutions including structured, unstructured, and knowledge graphs.

• Broad experience across NLP, ML, and Generative AI capabilities.