Machine Learning Infrastructure Engineer - (Multiple Levels)

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Job Category

Software Engineering

Job Details

About Salesforce

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Slack is looking for a Machine Learning Infrastructure Engineer to help us craft a robust and powerful platform to deliver artificial intelligence and machine learning experiences to our customers. You’ll be working on building robust, scalable, reliable, and efficient infrastructure to serve cutting edge foundational models as well as more traditional machine learning models. A great candidate will have experience both with ops/infra work to run services in the cloud and a solid understanding of AI/ML and its particular infra demands.

About The Role

Here at Slack, we believe we can build terrific product experiences for our customers with AI, letting them tap into their organizations’ collective knowledge. We have an opportunity to develop experiences that automate mundane tasks, efficiently find answers, and sift through the massive amount of information at a company to find what’s relevant for a particular worker. We’re investing in this area in a drive to make the work lives of the millions of knowledge workers who rely on slack day to day more productive and delightful.

The ML Services team, part of Slack’s Core Infrastructure organization, is responsible for delivering the platform, infrastructure, and expertise in ML/AI to make this product vision possible. We’ve built out much of this already as part of our Recommendation API, which you can read about here, but the needs of foundational AI models, with their unique development model and architectural needs, will require even further investment in our capabilities. We’re looking to hire machine learning infrastructure engineers who can help us deliver on that mission.

The sorts of things you might find yourself working on in this job:

• Working with our search team to generate embeddings at scale to power semantic search.
• Setting up our model training infrastructure to fine tune generative models while keeping our customer’s data secure.
• Managing deployments of machine learning models in our own kubernetes-based deployment system and through Sagemaker, working with tools like Chef and Hashicorp Terraform.
• Optimizing our models to reduce latency and handle spikes in traffic.

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
• Have experience developing, monitoring, and deploying systems in cloud environments like AWS, Azure, and GCP
• A related technical degree required
• Are curious, inquisitive, and determined to fix things when they break.
• Have experience with grafana, honeycomb, or other monitoring software
• Love to model modern methodologies for unit tests, code review, design documentation, debugging, and troubleshooting.
• Have 3+ years experience with software engineering.
• Have experience with ops tools and frameworks such as Terraform, Chef, and Kubernetes
• Worked on complex issues where the analysis requires an in-depth knowledge of the company and existing architecture.
• Have experience with ML model serving frameworks/toolkits like Kubeflow, MLflow, and Sagemaker
• Have built large-scale, distributed, production ML/AI systems professionally and can point to things you’ve worked on.
• Work well with a team of diverse backgrounds and experience on complicated projects.

Bonus Points

• You’re analytical and data driven
• You have experience developing machine learning models in PyTorch, Tensorflow, XGBoost, SciKit Learn or similar
• You have experience with building data pipelines in airflow, spark, and similar
• You have experience with vector based retrieval like through Vespa, Milvus, or Solr

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