AI Infrastructure Engineer

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

The AI Infrastructure team is at the heart of AI and ML at Scale - supporting all Scale products with our infrastructure, services and products. As a member of the AI Infrastructure team at Scale, you will be responsible for building and scaling the infrastructure that powers all machine learning at Scale. Additionally you will be developing a number of critical machine learning services used by all Scale products. This includes supporting the AI and ML workloads for products such as Enterprise Labeling, Rapid, Nucleus, Document AI and Content Understanding.

We are building a large hybrid human-machine system in service of ML pipelines for dozens of industry-leading customers. We currently complete millions of tasks a month, and will grow to complete billions of tasks monthly.

You will:

• Lead the evolution of our infrastructure to support Scale’s rapid growth.
• Develop best-in-class scaling, monitoring and observability tools for our infrastructure.
• Develop foundational ML services that will be used across Scale’s product portfolio.
• Be a self-starter who can own projects end-to-end, from requirements, scoping, design, to implementation.
• Have good taste in building systems and tools and know when to make build vs. buy trade offs, as well as having an eye for cost efficiency.
• Have attention to detail and a good sense for automation, debugging, and troubleshooting.

Ideally you'd have:

• 4+ years work experience.
• Experience with Python, Docker, Kubernetes, and Infrastructure as Code, CI/CD pipelines and monitoring tools.
• Experience managing, scaling and monitoring clusters in production.
• Experience building and deploying complex microservice architectures.
• Ability to design software and systems and ship high-quality code to production.
• Excellent communication skills and ability to work with multiple product leads.

Nice to have:

• Experience building machine learning training pipelines or inference services in a production setting.
• Experience with machine learning frameworks and libraries (e.g. PyTorch, TensorFlow).
• Experience with big data tools (e.g. Spark, Hadoop) and building ETL and streaming pipelines.