Slack is looking for a Machine Learning Engineer to craft and implement features, services, API methods, and models to leverage our data to make Slack a fabulous, robust, safe, and valuable product for our users. We work on applications across search, agentic systems, recommendation, and security, but ultimately are looking for engineers who can help drive impact with machine learning across the organization. Machine learning engineers at Slack touch a great variety of parts of our technical stack.

At different points, you might find yourself building data pipelines, training recommendation models, fine tuning LLMs, implementing features in our application, or analyzing experiment data. We don’t expect everyone to be an expert in everything, but we are looking for candidates with experience in Machine Learning, a strength in at least a couple of these, and who are excited to learn the rest. This is a practical machine learning team, not a research team.

Our goal is to deliver business value with machine learning and data in whatever form that takes. We are looking for engineers who are driven by driving impact for our business, building great products for our customers, and delivering robust, reliable services with machine learning. 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!) 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.

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. 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. Solid 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.

Bonus Points

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.

When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together,



we’ll bring the power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love.

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. It means that at Salesforce, we believe in equality for all. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law.

This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit.

The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education. At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. For Ontario-based roles, the base salary hiring range for this position is CAD 130,300 to CAD 179,200 annually.

The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable. It’s a platform that connects everyone in your business—employees, customers, and partners— securely with each other and integrates easily with apps you use every day to get work done. And everything happens, using any device, within a digital workspace that’s super easy to use.

📌 Software Engineer II, Machine Learning - Slack (Toronto)
🏢 110 salesforce.com Canada
📍 Toronto