Software Engineer - Production

About the role

Modern autonomous system development is heavily reliant on daily, realistic, and large scale simulations to test constant software changes. Join our cloud platform team to ensure we're building efficient and reliable systems for managing these large-scale workloads.

As a production engineer you'll be improving the development and deployment of our large-scale simulation infrastructure. The compute and data generation scale of our product workloads pushes the boundaries of standard cluster deployments, and you'll be at the forefront of helping build out and ensure reliability of this system. All while ensuring the deployment infrastructure is interoperable with various custom autonomy software and infrastructure used across the world.

You will work closely across the entirety of the engineering team to ensure the operational success of our backend and a variety of customer deployments. At Applied Intuition, we encourage all engineers to take ownership over technical and product decisions, interact closely with users to collect feedback, and contribute to a thoughtful, dynamic team culture.

At Applied Intuition, you will:

• Create and implement best practices for deploying and maintaining software for multiple customers across all three major cloud providers (AWS, GCP, Azure) with minimal downtime and high reliability
• Monitor Applied Intuition software on deployed machines and architect solutions to any bottlenecks that are encountered
• Improve developer efficiency by building internal tooling and optimizing our CI/CD systems

We're looking for someone who has:

• 5+ years of experience in a software engineering role working on infrastructure, monitoring, or a large scale software product
• 5+ years of coding experience from shell scripting (e.g., Bash) to higher-level languages (e.g., Python)
• Experience with microservice orchestration frameworks such as Kubernetes
• Experience working with containerized systems (e.g., Docker)

Nice to have:

• Experience utilizing open source tooling
• Experience with deploying software on either public clouds (e.g., AWS) or on-premise clusters