Software Engineer, Machine Learning (Multiple Levels) - Slack

About the position

Salesforce is looking for Staff and Senior level Machine Learning Engineers to craft and implement features, services, API methods, and models to leverage data to enhance Slack as a robust and valuable product for users. The role involves working on applications related to summarization, recommendation, ranking, and security, with a focus on driving impact through machine learning across the organization. The team aims to deliver business value with machine learning, whether through simple models or sophisticated solutions tailored to Slack's unique challenges. Engineers will engage in various tasks, including building data pipelines, training models, implementing features, and analyzing experiment data.

Responsibilities

• 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.
• Produce high-quality results by leading or contributing to large multi-functional projects.
• Actively own features or systems and define their long-term health.
• Support the development of sustainable data collection pipelines and management of ML features.
• Assist support and operations teams in triaging and resolving production issues.
• Mentor other engineers and conduct code reviews.
• Improve engineering standards, tooling, and processes.

Requirements

• 7+ years of applicable engineering experience.
• Experience with functional or imperative programming languages: PHP, Python, Ruby, Go, C, Scala, or Java.
• Experience 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.
• Experience working on generative AI apps with Large Language Models.
• Analytical and data-driven mindset with the ability to measure success with complex ML/AI products.
• Experience putting machine learning models into production at scale.
• Experience leading technical architecture discussions and driving technical decisions.
• Ability to write understandable, testable code with a focus on maintainability.
• Strong communication skills to explain complex technical concepts.

Nice-to-haves

• 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.

Benefits

• 401k
• 401k_matching
• dental_insurance
• health_insurance
• vision_insurance
• life_insurance
• disability_insurance
• paid_holidays
• unlimited_pto
• tuition_reimbursement
• professional_development
• employee_stock_purchase_plan
• conference_allowance
• continued_education