Software Engineer (AI Infrastructure)

Location: San Mateo or New York, United States

Salary: $175K–$220K annually, plus equity

Company

hirify.global provides a platform for building, training, and serving specialized AI models across text, image, embedding, audio, and multimodal workloads.

What you will do

• Architect and build scalable backend infrastructure for distributed training, inference, and data-processing pipelines.
• Design and implement job schedulers, resource managers, autoscalers, and model-serving services.
• Lead technical design discussions, mentor engineers, and establish infrastructure engineering best practices.
• Optimize compute costs, storage lifecycle management, network performance, system efficiency, and latency.
• Collaborate with ML, DevOps, product, and infrastructure stakeholders to translate research and product needs into robust solutions.
• Own systems from design through deployment and observability, ensuring high availability, fault tolerance, disaster recovery, and operational excellence.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• 5+ years of experience designing and building backend infrastructure in cloud environments such as AWS, GCP, or Azure.
• Experience with ML infrastructure and tooling, including technologies such as PyTorch, TensorFlow, Vertex AI, SageMaker, or Kubernetes.
• Strong software development skills in Python or C++.
• Deep understanding of distributed systems fundamentals, including scheduling, orchestration, storage, networking, and compute optimization.
• Experience with monitoring, alerting, logging, and tracing for system observability.

Nice to have

• Master’s or PhD in Computer Science or a related field.
• Experience leading infrastructure projects for large-scale ML/AI workloads or high-throughput systems.
• Familiarity with Terraform, ArgoCD, GitOps, infrastructure-as-code, and CI/CD tooling.
• Contributions to open-source cloud or ML infrastructure projects.

Culture & Benefits

• Work on challenging AI infrastructure problems, including low-latency inference and scalable model serving.
• Build production technology using advanced cloud-native and open-source tools such as Kubernetes, Kubeflow, and MLFlow.
• Collaborate with experienced engineers and AI researchers in a fast-growing environment.
• Compensation includes equity.