Software Engineer Ii, Machine Learning (search) - Slack

Salesforce Salesforce · Enterprise · Toronto, Canada, Canada

Salesforce is seeking a Machine Learning Engineer for their Slack division to develop and implement ML models for ranking, retrieval, and generative AI use cases. The role involves building data pipelines, fine-tuning LLMs, and deploying ML artifacts at scale to enhance Slack's product for millions of daily active users. This is a practical, impact-driven role focused on delivering business value with ML.

What you'd actually do

  1. Develop ML models supporting ranking, retrieval, and generative AI use-cases.
  2. Brainstorm with Product Managers, Designers and Frontend Engineers to conceptualize and build new features for our large (and growing!) user base.
  3. Produce high-quality results by leading or contributing heavily to large multi-functional projects that have a significant impact on the business.
  4. Actively own features or systems and define their long-term health, while also improving the health of surrounding systems.
  5. Support in the development of sustainable data collection pipelines and management of ML features.

Skills

Required

  • 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 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.
  • Led 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.
  • A bachelor's degree in Computer Science, Engineering, Statistics, Mathematics or a related field, or you have equivalent training, fellowship, or work experience.

Nice to have

  • Experience building and optimizing RAG pipelines.
  • Expertise in conversational agents.
  • Expertise in retrieval systems and search algorithms.
  • Familiarity with vector databases and embeddings.
  • Broad experience across NLP, ML, and Generative AI capabilities.

What the JD emphasized

  • put machine learning models or other data-derived artifacts into production at scale
  • fine tuned them
  • conversational agents
  • retrieval systems and search algorithms

Other signals

  • develop ML models supporting ranking, retrieval, and generative AI use-cases
  • put machine learning models or other data-derived artifacts into production at scale
  • fine tuning LLMs
  • conversational agents
  • retrieval systems and search algorithms