Staff Software Engineer (AI)

TL;DR

Staff Software Engineer (Django/Python): Own and evolve the core platform powering AI employees with an accent on backend systems, distributed task infrastructure, event-driven architecture, and Kubernetes deployments. Focus on optimizing database queries, scaling async workflows, improving observability, and driving architectural decisions for reliability and scalability.

Location: Remote (overlap with Americas timezones for collaboration; reliable high-speed internet)

Company

Supernal helps small-to-medium businesses hire their first AI employee using intelligent agentic workflows on a proprietary platform delivering value-generating AI teammates for real business processes.

What you will do

• Drive platform architecture decisions, align team on scalable patterns, and ensure long-term maintainability
• Review code, design docs, and proposals for scalability, reliability, security, and operability
• Mentor engineers, unblock issues, raise production readiness bar, and establish best practices
• Evolve Django/DRF/ASGI backend for performance and correctness
• Scale async execution with Celery, Dramatiq, Temporal; implement resilient workflows
• Optimize PostgreSQL/pgvector, caching; maintain Kubernetes (GKE, Helm), CI/CD, autoscaling
• Own RabbitMQ, Redis, PostgreSQL reliability; lead incident response and post-mortems
• Extend OpenTelemetry, Datadog instrumentation, dashboards, alerts, SLOs; profile bottlenecks

Requirements

• 10+ years building and operating production backend systems at scale
• Deep expertise in Python (Django preferred) and relational databases (PostgreSQL)
• Hands-on with Kubernetes, Helm, cloud infrastructure (GCP preferred)
• Strong background in distributed systems: message queues, event sourcing, workflow orchestration
• Production experience with async task systems (Celery, Dramatiq or similar)
• Track record debugging complex production issues across services
• Ability to work autonomously and drive initiatives
• Clear technical communication to explain tradeoffs and build consensus

Nice to have

• Experience with Temporal or similar workflow engines
• Background in LLM infrastructure, RAG systems, or AI/ML platforms
• Familiarity with OpenTelemetry, Datadog observability stacks
• Experience with KEDA or Kubernetes autoscaling
• Contributions to multi-tenant SaaS platform architecture
• History improving developer experience and platform abstractions