Tech Stack
AWSAzureCloudDistributed SystemsGoogle Cloud PlatformGRPCKafkaKubernetesMicroservicesTerraform
About the role
- 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)
- 3–5 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