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RadixArk

Open-source AI infrastructure for large-scale inference

Technical Program Manager

Program ManagementConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
  • At least 4 years of direct experience in Technical Program Management, Engineering Management, or a senior engineering role with significant program ownership, in a software or infrastructure company.
  • Strong technical fluency in systems software, distributed systems, or AI/ML infrastructure, including the ability to read code, follow architecture discussions, and challenge technical assumptions productively.
  • A demonstrated track record of shipping complex, multi-team programs on time, including managing dependencies, risks, and scope changes.
  • Excellent written and verbal communication skills, with the ability to drive alignment across engineers, executives, and external partners.

Responsibilities

  • Drive end-to-end execution of large-scale, cross-functional programs spanning inference engines, training frameworks, and hardware integration efforts.
  • Define program structure, including milestones, dependencies, critical paths, risks, and success criteria, and maintain a clear source of truth for stakeholder status.
  • Run design reviews, sprint planning, release readiness reviews, and post-mortems; document decisions and close out follow-ups.
  • Identify and unblock cross-team dependencies across kernel, runtime, scheduler, networking, and model teams before they become release blockers.
  • Drive release management for major versions, including changelog ownership, compatibility validation, partner rollout sequencing, and rollback planning.
  • Partner with Product Management to translate roadmap priorities into executable program plans with clear scope, staffing, and timelines.
  • Work with engineering leads on technical trade-off decisions, understanding architecture deeply enough to ask informed questions and surface risks.
  • Coordinate hardware enablement programs with partners such as Nvidia, Google, and AWS, including new accelerator bring-up, kernel co-development, and benchmark validation.
  • Manage integration programs with frontier AI labs and early adopters, ensuring technical requirements, SLAs, and feedback loops are well-defined.
  • Build and improve the engineering operating cadence, including standups, planning rituals, OKR tracking, dashboards, and reporting to leadership.
  • Establish metrics and instrumentation for program health, including velocity, defect rates, benchmark regressions, and customer-reported issues, and drive accountability against them.
  • Lead incident response coordination for production issues affecting partners; own root-cause review and corrective-action tracking.
  • Improve developer productivity by identifying and removing systemic friction in build, test, and release pipelines.
  • Connect engineering, product, GTM, and external partners, ensuring stakeholders have the right information at the appropriate level.
  • Produce clear written updates for leadership and partners, translating engineering progress into business-relevant signals.
  • Represent program status honestly, including risks and slips, with concrete mitigation plans.

Desired Qualifications

  • Direct experience shipping AI/ML infrastructure such as inference engines, training frameworks, GPU kernels, distributed schedulers, or model serving platforms.
  • A hands-on coding background in Python, C++, or CUDA and comfort working in engineering codebases, including reading pull requests, running benchmarks, and reproducing issues.
  • Experience coordinating with hardware vendors such as Nvidia, AMD, Google TPU, or AWS Trainium/Inferentia on enablement or co-engineering programs.
  • Experience driving open-source release programs or working in open-source communities, including issue triage, RFC processes, and contributor coordination.
  • Familiarity with release engineering, CI/CD systems, and observability tooling for large-scale distributed systems.
  • Experience supporting B2B or developer-facing products with enterprise SLAs.

About the Company

RadixArk

Open-source AI infrastructure for large-scale inference

RadixArk develops large-scale AI infrastructure for training and deploying models. Its core offering is the open-source SGLang engine that accelerates model inference, complemented by Miles, an open-source framework tailored for large-scale reinforcement learning and post-training tasks. The company provides managed infrastructure and production-grade tooling for model inference and post-training, with additional commercial products built on top of its open-source foundations. Revenue comes from managed hosting services and other paid offerings. Unlike many competitors, RadixArk pursues an open-core model to keep its core technology open and accessible to researchers, startups, and enterprises while monetizing via hosted services and premium tools. The goal is to democratize frontier AI infrastructure, reducing duplicated effort and enabling the AI community to train, deploy, and operate advanced models more efficiently and affordably.

Company size
51-200
Company stage
Seed
Total funding
$100M
Headquarters
Palo Alto, CA
Founded
2025

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