Senior Platform/Infrastructure Engineer

Location: Fully remote (HQ Cambridge, MA)

Hours: 9–5 EST, with 2-day on-site visits every 6 weeks

You’ll be responsible for designing, scaling, and maintaining the infrastructure and internal developer platforms that power a real-time learning AI at a seed-stage startup. The role blends infrastructure ownership with platform engineering to enable AI/product teams to ship quickly and reliably.

Key Responsibilities - Infrastructure

• Maintain production health: performance, reliability, cost efficiency, and security.
• Manage GCP Kubernetes clusters (GKE), networking, storage, and compute resources.
• Handle scaling, resource allocation, and high availability for growing customer demand.
• Refine observability: logs, traces, metrics, dashboards, and alerts.
• Perform security hardening and cost optimization.

Kep Responsibilities - Platform Engineering

• Build internal tooling and abstractions for developer productivity.
• Design CI/CD pipelines using GitHub Workflows and ArgoCD.
• Provide self-service environments, internal portals, and deployment systems.

Collaboration & Communication

• Work closely with AI and full-stack teams to optimize system architecture.
• Explain technical concepts and trade-offs clearly to engineers and non-engineers.
• Troubleshoot issues across multiple systems (Python, JavaScript, SQL).

Requirements

• 5+ years in production cloud environments at scale.
• Strong familiarity with GCP (primary) and some AWS experience.
• Experience with Kubernetes (GKE), node pools, and memory-intensive jobs.
• Working knowledge of CI/CD systems (GitHub Workflows + ArgoCD).
• Exposure to observability tools (Datadog), databases (Cloud SQL, ClickHouse, Bigtable), and cloud services.

Skills & Qualities

• Strong analytical and problem-solving ability.
• Clear, collaborative communication.
• Curiosity and ownership mentality.
• Fluent in reading/debugging code across Python, JavaScript, SQL.

Technical Stack

• Cloud: GCP (primary), AWS (secondary)
• Kubernetes: GKE, multiple node pools
• CI/CD: GitHub Workflows + ArgoCD
• Data: Cloud SQL, ClickHouse, Bigtable, GCS, Dataflow
• Networking: Cloudflare Workers, Durable Objects, WebSocket communication
• Monitoring: Datadog
• Environments: Production, Staging, Integration, Development