Slack is looking for a Senior Staff Machine Learning Engineer to craft and implement ML and generative AI powered features that leverage our data to make Slack a fabulous, robust, safe, and valuable product for our users. Our team has built out robust functionality spanning LLM deployment, evaluation, monitoring and quality improvements. We are looking for an architect level engineer with experience in the development of both traditional ML and more recent generative AI solutions to help guide the architecture and development of AI at Slack.

What you will be doing
Brainstorm with Product Managers, Designers and 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.
Help other engineers actively own features or systems and define their long-term health, while also improving the health of surrounding systems.
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:
10+ years experience with machine learning and software engineering.
Put machine learning models, generative AI or other data-derived artifacts into production at scale, especially for text-based applications.
Worked on generative AI apps with Large Language Models and possibly fine tuned them or improved quality through other methods.
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, SQL, 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.
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:
Deployed production RAG pipelines
Experience with LLM evaluation and monitoring at scale
Experienced in A/B testing and experimentation
Knowledge of leveraging multiple data types in RAG solutions including structured, unstructured, and graph.

Accommodations

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