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Senior Staff Software Engineer, Machine Learning - Slack

Georgia, United States (On-site)

Senior Staff Software Engineer, Machine Learning - Slack

1 Month ago • 10 Years +

About the job

Summary_{By Outscal}

Slack is seeking a Senior Staff Machine Learning Engineer to develop AI-powered features. Ideal candidate has 10+ years experience in ML, generative AI, production deployments, and building with frameworks like pytorch, Tensorflow, and Spark. Must have experience with Large Language Models and deploying them at scale for text-based applications.

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Job Category

Software Engineering

Job Details

About Salesforce

We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.

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.

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