Senior Software Engineer, Infrastructure (Dev Prod/Platform; NOT DevOps)

Key Responsibilities

• Scalable Automation Frameworks: Architect modular, reusable CI/CD and distributed testing frameworks that provide rapid, automated feedback loops for code quality, security, and performance directly within the developer workflow.
• Engineering Self-Service & Tooling: Design and maintain internal developer platforms, frameworks and tools ecosystem that automates builds, tests, releases.
• Design & Build Distributed Systems: Build the task-queue and orchestration layer to manage nTop worker lifecycles.
• Hybrid-Cloud Infrastructure: Develop "run-anywhere" deployment patterns using Kubernetes, Helm, and Terraform that work seamlessly across AWS, GCP, and on-premise hardware.
• Container Security & Isolation: Implement secure execution environments for untrusted code/designs using technologies like gVisor or Kata Containers.
• Production CI/CD: Design and maintain high-performance build pipelines in Jenkins and GitHub Actions.
• Deployment Tooling: Develop and support Helm charts and Terraform modules that allow our clients to deploy in their own private clouds or on-prem data centers.
• Storage Abstraction: Build or integrate S3-compatible storage layers to handle high-throughput telemetry and design file I/O.

Required Skills & Experience

• Kubernetes & Docker: Deep experience building production-grade containerized environments and managing K8s resources (Deployments, Jobs, HPA).
• CI/CD Engineering: Expert at managing Jenkins (Shared Libraries, Pipelines-as-Code) and integrating with GitHub for automated testing and release management.
• Infrastructure as Code: Proficiency with Terraform for managing cloud-agnostic infrastructure.
• Scripting & Tooling: Strong proficiency building developer productivity tools, automation frameworks, scripts, and worker services.

Nice to Have

• MLOps Familiarity: Knowledge supporting Machine Learning workflows, including model serving and model management (MLflow, Weights & Biases).
• AI Integration: Experience or interest in deploying or optimizing LLM-based applications, RAG pipelines, or vector databases.
• GPU Infrastructure: Experience in managing GPU-enabled compute clusters.

Bonus Points

• Experience with GPU-accelerated workloads.
• Experience with AI/ML technologies, Evals Infra.

Benefits

• Outstanding PTO and leave policy.
• ISO options.
• Healthcare: Medical, Dental and Vision plans.
• 401k with generous matching.
• Annual stipend for continued career learning/development.
• Commuter benefits for NY based hires.