Software Engineer

Prelude

12d•Remote

About The Position

Origin is building the endpoint AI observability platform for AI-adopting organizations. We believe that organizations should not adopt AI on their endpoints without observability in place, and we want to be the platform that maximizes the productivity & safety of that adoption. The company exists to maximize the diffusion of intelligence inside an organization, maximize the productivity of agentic systems and the humans driving them, keep agent actions traceable end-to-end so that when a user asks for something in a prompt they can verify the agent actually did what was expected, and give organizations one interface to answer the hardest questions they have about how work gets done. Our platform monitors and protects some of the most important organizations in the world. We are backed by Sequoia Capital, Brightmind Ventures, IA Ventures and other top firms.

Requirements

AI-native engineering practice: prolific use of Claude Code, Codex, and OpenCode, with the judgment to know when an agent will go off the rails and the skill to redirect it
Track record of building data-intensive systems at scale (millions of concurrent operations, high-throughput event processing, distributed coordination)
Deep understanding of distributed systems principles: streaming, batching, backpressure, recovery, and consistency models
Performance engineering mindset: complexity analysis, profiling-driven optimization, and discipline under resource constraints
Modern systems programming experience, Rust preferred (we value problem-solving ability over specific language expertise)
Strong working fluency in Python across services, inference pipelines, and tooling
Production experience with cloud infrastructure (AWS preferred), Kubernetes (EKS or equivalent), and event streaming (Kafka or equivalent)
Comfortable working in our React frontend to deliver functionality to end users
High agency, with a track record of identifying important work and finishing it

Nice To Haves

Background in real-time monitoring or observability infrastructure (Palantir, Datadog, Honeycomb, Chronosphere, the data side of Stripe or Cloudflare)
Background in high-throughput systems (high-frequency trading, real-time telemetry, low-latency distributed systems)
Background in AI infrastructure or applied ML, particularly inference pipelines, embedding and topic systems, or agent orchestration
Prior founding-engineer or early-stage startup experience
Open-source contributions or published research in AI tooling, agent frameworks, or developer infrastructure
Data engineering experience with columnar databases or OLAP / time-series systems
Deep OS internals knowledge (Windows ETW, Apple ESF, eBPF)
Cross-platform systems expertise (Windows and macOS)

Responsibilities

Obsess over customer problems and ship for velocity and impact over technical pedantry
Ship across the stack - agent, backend, infrastructure, and frontend - wherever the work is that week
Build internal agents that automate the repeatable parts of engineering and distribute them across the team
Define how AI-native engineering works at Origin: the context, permissions, and tooling our agents need to do real work
Design and operate distributed systems using AWS, EKS, Clickhouse, and Kafka against real production SLAs
Drive technical decisions on storage schemas, API contracts, and the patterns that hold up at scale in a multi-tenant environment
Work alongside research and systems-internals engineers to turn their work into reliable production software