MTS · Engineering · Distributed Systems

Nebula is the system of state for enterprise agent workflows. This role owns the storage and execution systems beneath it: object-storage-native architecture, distributed execution, partitioning, multi-region deployment, and customer-managed infrastructure. It's a product engineering role, not general DevOps or internal-platform work.

What you'll work on

• Evolve Nebula's S3-backed storage architecture, including logs, manifests, snapshots, indexes, checkpoints, caching, and compaction.
• Build durable ingestion and workflow execution on top of queues, including idempotency, retries, backpressure, replay, dependency scheduling, and partial-failure recovery.
• Design partitioning and routing across tenants, workers, Kubernetes clusters, storage partitions, and regions.
• Define clear consistency, durability, and recovery guarantees across asynchronous pipelines and replicated object storage.
• Improve read-path performance and keep p95 and p99 latency predictable as datasets and workloads grow.
• Build multi-region replication, routing, failover, and recovery mechanisms.
• Detect and recover from queue buildup, retry storms, stale indexes, data drift, hotspots, noisy tenants, and silent correctness failures.
• Operate the same core data plane across our SaaS, BYOC, on-premises, and air-gapped deployments.
• Work directly with the founders and early customers to turn scale, reliability, and deployment constraints into product architecture.

What we're looking for

• Production experience building distributed systems, storage systems, databases, workflow engines, search or indexing infrastructure, or high-throughput backend systems.
• Experience owning stateful systems where correctness, latency, durability, and cost mattered simultaneously.
• Strong judgment around consistency, concurrency, persistence, caching, partitioning, replication, and failure recovery.
• Experience with queues and asynchronous execution, including retries, idempotency, ordering, replay, and backpressure.
• Hands-on experience with Kubernetes and cloud infrastructure.
• Experience debugging difficult production problems such as tail latency, queue buildup, retry storms, storage bottlenecks, data drift, hotspots, and partial failures.
• High ownership and comfort turning ambiguous systems problems into explicit invariants, guarantees, and measurable outcomes.

We care more about demonstrated systems judgment than a particular number of years of experience.

Nice to have

• Experience with Rust, Go, C++, or another systems-oriented language.
• Experience with object-storage-native databases, distributed databases, event streaming, workflow orchestration, vector indexes, graph systems, or search infrastructure.
• Experience with multi-region systems and asynchronous replication.
• Experience supporting BYOC, on-premises, air-gapped, or regulated customer environments.
• Experience building infrastructure for AI agents or other data-intensive AI applications.

Why Zeroset

• Own core systems that directly determine the reliability, performance, and economics of the product.
• Help define a new architecture for durable agent state rather than maintaining a mature system with fixed assumptions.
• Work with a small, technical team with high trust, low bureaucracy, and direct access to customers and founders.
• Take on difficult storage and distributed-systems problems at the center of production enterprise AI.