Backend Software Engineer - MLOps & Cloud Infrastructure
Listed on 2026-02-14
-
IT/Tech
AI Engineer, Machine Learning/ ML Engineer
Position Summary
Location:
Lehi, Utah - In Person
Basis:
Full-Time
Salary: $100,000
About Spot ParkingSpot Parking is building next generation parking infrastructure powered by computer vision and real time data. We deploy network cameras, run ML inference at scale, and transform raw video into actionable enforcement and analytics systems.
We operate fast, ship often, and solve hard infrastructure problems that sit at the intersection of edge devices, cloud compute, and applied machine learning.
The RoleWe are hiring a Backend Engineer focused on MLOps and Cloud Infrastructure
. You will own critical backend systems that deploy, serve, scale, support, and monitor our computer vision models.
You will design and build core infrastructure that directly impacts performance, reliability, and cost at scale.
You should be highly autonomous
, comfortable making decisions, and excited about solving new and ambiguous technical problems.
- Building and maintaining data pipelines between network cameras, databases, and inference clusters
- Deploying and scaling ML models across AWS compute environments
- Designing and optimizing backend services and APIs
- Implementing deployment workflows, validation systems, and load testing
- Optimizing compute cost across AWS infrastructure
- Dockerizing services and deploying containerized workloads
- Improving CI/CD workflows and deployment automation
- 3+ years of industry experience in backend, cloud, or MLOps roles
- Authorized to work in the United States
- Located in or near Lehi, UT or willing to relocate
- Deep understanding of AWS and cloud compute architecture
- Strong Python experience
- FastAPI
- SQL Alchemy
- Boto3
- Comfortable working with AI tools and leveraging them to move faster
- Self-motivated and able to work quickly and autonomously
- Terraform or other Infrastructure as Code experience
- CI/CD and automated testing experience (i.e. Git Hub Actions)
- Docker and Kubernetes experience
- Experience deploying and monitoring production ML systems
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