Design, build, and scale core components of Voxel’s perception and computer vision systems, enabling real-time understanding of safety and operations in industrial environments
Lead the development of distributed systems for ingesting, processing, and analyzing video data across edge devices and cloud infrastructure
Develop secure, scalable backend services and APIs to power our video platform, including access control, data pipelines, and event detection services
Contribute to the architecture of low-latency, resilient services supporting real-time alerts, historical analytics, and ML inference workflows
Partner with ML engineers, product managers, and platform teams to deploy and operationalize machine learning models in production
Take ownership of key technical areas, influence design decisions, and mentor junior engineers when needed
Uphold engineering best practices in code quality, testing, monitoring, and operational excellence
Requirements
Bachelor's degree in Computer Science, Software Engineering, or a related technical field (or equivalent experience)
5+ years of experience designing and building backend systems using REST/gRPC APIs, microservices, and containerized architectures
Proven track record of building and scaling distributed systems with real-time data processing using tools like Kafka, Flink, or Kinesis
Strong familiarity with deploying production systems in cloud environments such as AWS, GCP, or Azure
Experience using Kubernetes to deploy, scale, and manage containerized applications in production
Hands-on experience with infrastructure-as-code tools like Terraform or CloudFormation
Exposure to ML or computer vision pipelines, especially around data ingestion, preprocessing, or inference integration
Comfortable leading small technical projects or independently driving a component from design through deployment
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