Software Engineer, Machine Learning (Ai Engineering)

TL;DR

Software Engineer, Machine Learning (AI Engineering): Crafting and implementing features, services, API methods, and models to leverage data and make Slack a fabulous, robust, safe, and valuable product for users with an accent on driving impact at the forefront of conversational intelligence. Focus on building data pipelines, training recommendation models, fine tuning LLMs, implementing features, or analyzing experiment data.

Location: Remote

Company

Slack is a communication platform that connects people with the information they need.

What you will do

• Leverage machine learning and artificial intelligence subject matter expertise to drive improvements in the Slackbot experience.
• Develop ML models supporting ranking, retrieval, and generative AI use-cases.
• Conceptualize and build new features for large user base in collaboration with Product Managers, Designers, and Frontend Engineers.
• Actively own features or systems and define their long-term health, while also improving the health of surrounding systems.
• Support the development of sustainable data collection pipelines and management of ML features.
• Mentor other engineers and deeply review code.

Requirements

• Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala, or Java.
• Experience building with common ML frameworks like PyTorch, Tensorflow, Keras, XGBoost, or Scikit-learn
• Experience fine tuning LLMs or BERT models.
• Experience building batch data processing pipelines with tools like Apache Spark, Hadoop, EMR, Map Reduce, Airflow, Dagster, or Luigi.
• An analytical and data driven mindset, and know how to measure success with complicated ML/AI products.
• Strong communication skills and you are capable of explaining complex technical concepts to designers, support, and other specialists.

Nice to have

• Expertise in conversational agentic systems.
• 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.