- 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

- Sometimes that means bootstrapping something simple like a logistic regression and moving on

- Other times that means developing sophisticated, finely tuned models and novel solutions to Slack’s unique problem space

- We are looking for engineers who are driven by driving impact for our business, building outstanding products for our customers, and delivering robust, reliable services with machine learning

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

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- 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

Benefits

- Medical Care

- Life Insurance

- Retirement Savings

- Employee Assistance Programs
- With 9 standard holidays and four floating holidays, you get a total 13 paid days off each year- - Built with common ML frameworks like pytorch, Tensorflow, Keras, XGBoost, or Scikit-learn

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- Led technical architecture discussions and helped drive technical decisions within the team

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

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

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

- - Fine tuned LLMs or BERT models

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

- - 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

- - A bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics or a related field, or you have equivalent training, fellowship, or work experience

- - Expertise in conversational agentic systems

- - Expertise in retrieval systems and search algorithms

- - Broad experience across NLP, ML, and Generative AI capabilities

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

- - Familiarity with vector databases and embeddings

📌 Software Engineer (Machine Learning - Slack) (Toronto)
🏢 Salesforce
📍 Toronto