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Manifold Labs is building the backbone of permissionless intelligence — a global compute market where companies and individuals can access world-class hardware at best-in-class pricing.
Our flagship product, Targon, is a permissionless, verifiably confidential AI cloud. It serves thousands of users with high-performance AI infrastructure, including B300s, B200s, H200s, H100s and RTX Pro 6000s. Security is at the core of everything we build, leveraging NVIDIA Confidential Computing and Intel TDX to power our own Targon Virtual Machine (TVM) and TargonOS to keep enterprise data encrypted at rest, in transit, and even while it is being processed. We are a small team focused on engineering excellence, backed by a $10.5M Series A, and we are now scaling Targon, Sybil, and the rest of our ecosystem to enterprise customers.
We're hiring a Software Engineer - Infrastructure to help build and scale the APIs, services, and infrastructure that power the Targon ecosystem. You'll work on performance-critical systems at the heart of a permissionless compute network — designing services that schedule and route workloads across thousands of GPUs, optimizing for latency and throughput, and building new capabilities from scratch. This is a deeply technical role for an engineer who cares about systems that are fast, reliable, and secure at scale.
You'll be focused on:
Developing and maintaining the API layers and backend services that power Targon, Sybil, and the broader ecosystem.
Building and scaling the systems that orchestrate confidential workloads across a distributed network of GPUs, including TVM and TargonOS attestation and scheduling paths.
Benchmarking existing and new services to identify bottlenecks and ship measurable performance and reliability improvements.
Designing and operating resilient, observable infrastructure that keeps enterprise-grade latency and throughput guarantees.
Creating new services and applications from scratch to expand the Targon platform.
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Proficiency with Golang for backend development.
Experience working with Kubernetes in production.
Familiarity with Python for scripting and tooling.
Strong background with Unix/Linux systems.
Experience with distributed systems, networking, or high-throughput service design.
Exposure to confidential computing, TEEs, or secure infrastructure.
A bias toward ownership — comfortable taking an ambiguous problem and driving it to a shipped, well-tested solution.
Decentralized AI infrastructure for secure, confidential cloud compute.
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