Zaimler7mo ago

Software Engineer, Distributed Systems

San MateoFull Timemid

OtherDevOps & InfrastructureSoftware EngineerEngineerSoftware Engineering

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Quick Summary

Requirements Summary

resource contention, split-brain scenarios, cascading failures. Deep experience building distributed systems at scale: high QPS environments, multi-region deployments,

Technical Tools

OtherDevOps & InfrastructureSoftware EngineerEngineerSoftware Engineering

About zaimler

AI agents can't reason over data they don't understand. Enterprise data today is fragmented across dozens of systems with no shared context, meaning, or structure, and that's why most enterprise AI is failing. The shift from copilots to autonomous agents is creating an entirely new infrastructure layer, and we're building it.

zaimler is the context infrastructure for the agentic era: a platform that automatically discovers domain knowledge, maps relationships, and gives AI agents the semantic understanding to operate with precision at scale. Imagine knowledge graphs that support real-time inference, built for systems that need to reason, not just retrieve.

zaimler was founded by Biswajit Das (ex-VP Engineering, Truera), a Data Infra veteran and former Chief Architect at Visa, and Sofus Macskassy (ex-Director of Engineering, LinkedIn), who built one of the largest knowledge graphs in production in the industry at LinkedIn. We're a small, senior team at the seed stage, deploying with major enterprises across insurance, travel, and technology. If you want to build infrastructure that the next decade of AI runs on, we'd love to talk.

The Role

We’re looking for a Software Engineer, Distributed Systems to own the core infrastructure layer that makes our semantic platform fast, durable, and scalable. You’ll be building primitives: storage engines, consistency protocols, query execution layers, and coordination services that operate reliably at scale.

Design and build distributed storage and coordination systems from first principles, fault-tolerant, strongly consistent, and performant under high load.

Own consistency and availability tradeoffs at the data layer, implement and tune consensus protocols for production workloads.

Build and optimize query execution engines for knowledge graphs.

Architect data partitioning and replication strategies to meet throughput and durability SLAs.

Work at the system level, tune I/O scheduling, network stacks, and memory management for distributed workloads.

Collaborate with ML and product engineers to ensure the data layer meets the latency, throughput, and consistency requirements of agentic AI workloads.

Debug and harden distributed systems in production: resource contention, split-brain scenarios, cascading failures.

Deep experience building distributed systems at scale: high QPS environments, multi-region deployments, consistency and availability tradeoffs.

Fluency in Java, C++, or Rust; comfortable working at the systems level.

Strong grasp of consistency models, consensus protocols, and replication strategies.

Experience with traffic and data partitioning, network protocol design, and database internals.

Hands-on Linux systems, distributed file systems, OS internals.

Ability to build durable, observable, and operationally sound systems that hold up under real load.

Experience building or deeply understanding query engines (OLAP/OLTP), ingestion pipelines, or storage layers.

Kubernetes and cloud infrastructure (AWS, Azure, GCP) at production scale.

Prior work on knowledge graphs, graph databases, or semantic data layers.

Track record of shipping systems that other engineers depend on.

A rare chance to be a founding engineer shaping both company and product direction.