Staff Software Engineer - Backend

GoDaddy

2d•$140,000 - $273,000•Remote

About The Position

At GoDaddy, we're building the next generation of AI and machine learning capabilities that power experiences for millions of entrepreneurs worldwide. Our Machine Learning Engineering team bridges the gap between research and production, transforming cutting-edge models into scalable, reliable, and observable services that operate at global scale. We're looking for a Staff Software Engineer to lead the design and evolution of the infrastructure, platforms, and services that enable machine learning models to run in production. This is a highly technical, hands-on role where you'll work closely with ML Scientists, Data Engineers, and Product teams to deliver robust ML-powered solutions while helping shape the future of our machine learning platform. As a senior technical leader, you'll influence architecture, drive engineering excellence, and mentor engineers across a globally distributed team.

Requirements

7+ years of software engineering experience building and operating large-scale, production-grade distributed systems and microservices.
Strong proficiency in Python, Go, and/or TypeScript, with deep expertise in API design, system architecture, scalability, resiliency, and performance optimization.
Hands-on experience building CI/CD pipelines, cloud-native applications, and infrastructure on AWS using services such as ECS, EKS, Lambda, DynamoDB, S3, IAM, and CloudWatch.
Experience with containerization and orchestration technologies including Docker, Kubernetes, ECS, or similar platforms supporting high-availability production workloads.
Proven ability to lead complex technical initiatives, influence architecture, collaborate across diverse stakeholders, and mentor engineers in a fast-paced environment.

Nice To Haves

Experience deploying and operating machine learning or generative AI workloads using technologies such as vLLM, Triton, TorchServe, SageMaker Endpoints, or similar serving frameworks.
Familiarity with modern observability practices and tools including OpenTelemetry, Prometheus, Grafana, and CloudWatch.
Experience with vector databases, feature stores, caching technologies (Valkey/Redis), and infrastructure-as-code solutions such as CDK, CloudFormation, or Terraform.
Knowledge of GPU infrastructure management, workload scheduling, performance tuning, and cloud cost optimization strategies.
Experience serving as a technical lead or mentor for distributed engineering teams and leveraging AI-assisted development tools to accelerate software delivery.

Responsibilities

Deploying and operating machine learning or generative AI workloads using technologies such as vLLM, Triton, TorchServe, SageMaker Endpoints, or similar serving frameworks.
Implementing modern observability practices and tools including OpenTelemetry, Prometheus, Grafana, and CloudWatch.
Utilizing vector databases, feature stores, caching technologies (Valkey/Redis), and infrastructure-as-code solutions such as CDK, CloudFormation, or Terraform.
Managing GPU infrastructure, workload scheduling, performance tuning, and cloud cost optimization strategies.
Serving as a technical lead or mentor for distributed engineering teams and leveraging AI-assisted development tools to accelerate software delivery.