Software Engineer II, Machine Learning - Slack
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About this role
Employer-provided description, formatted for easier reading.
Job Description Summary
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.
At Slack, that impact can be huge:
- We have over 10 million daily active users relying on our product.
- At peak usage, a million messages a minute pass through Slack.
- During the week, our users spend over a billion minutes a day active in our product.
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 great products for our customers, and delivering robust, reliable services with machine learning.
What you will be doing:
- 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!) 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.
You may be a fit for this role if you have:
- 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.
- Fine tuned LLMs or BERT models.
- 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.
Bonus Points
- 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.