RL Infrastructure Engineer — Frontier AI Research

Aionia Group; San Francisco (CA); On-site; USD 300,000 - 500,000; Full time

Job summary

Aionia Group in San Francisco is seeking a Systems Infrastructure Engineer to build scalable infrastructure for RL experiments. This role offers a unique opportunity to work on innovative projects with leading researchers in a well-funded AI company.

The ideal candidate has over 2 years of experience in RL systems, a degree in a related field, and a passion for scalable solutions. Competitive compensation includes a base salary of $300K–$500K plus equity.

Qualifications

• 2+ years building infrastructure for LLM or RL systems.
• Experience at a high-engineering-bar organization.
• Experience with GPU clusters, distributed training, and high-throughput inference systems.

Responsibilities

• Design and deploy infrastructure for distributed RL training and inference.
• Improve reliability and throughput for large-scale RL experiments.
• Establish engineering standards for RL infrastructure.

Skills

Building infrastructure for LLM or RL systems

Hands-on experience with GPU clusters

Familiarity with modern LLM-RL training frameworks

Curiosity and hypothesis-driven thinking

Education

Degree in CS, EECS, Mathematics, or a related field

Tools

vLLM

SGLang

veRL

SkyRL

DeepSpeed

Job description

A rare infrastructure role in a frontier RL research operation.

Compensation: $300K–$500K base + equity. San Francisco, on-site. Hiring urgent.

Opportunity

In a seed‑stage, well‑funded AI company, a small engineering team works with top researchers to automate task objectives and scale learning curriculums across thousands of GPUs.

Own the Systems Layer

This role builds the systems layer that enables researchers and applied ML engineers to run, debug, and reproduce large‑scale RL experiments—including distributed rollouts, training orchestration, inference, evaluation, data pipelines, observability, and reliability.

You will own infrastructure projects end‑to‑end, translating experimental workflows into durable, scalable solutions alongside leading RL minds.

Responsibilities

• Design and deploy infrastructure for distributed RL training and inference across thousands of GPUs.
• Improve reliability, debuggability, and throughput for large‑scale RL experiments.
• Build interfaces for researchers and ML engineers to launch, inspect, compare, and reproduce experiments.
• Eliminate bottlenecks in training, rollout generation, evaluation, data movement, and cluster utilization.
• Establish engineering standards for RL infrastructure: testing, observability, versioning, and reproducibility.

Qualifications

• 2+ years building infrastructure for LLM or RL systems.
• Experience at a high‑engineering‑bar organization—top AI startups, frontier labs, or Big Tech RL research teams.
• Hands‑on experience with GPU clusters, distributed training, model serving, or high‑throughput inference systems.
• Familiarity with vLLM, SGLang, and modern LLM‑RL training frameworks.
• Degree in CS, EECS, Mathematics, or a related field.
• Very high level of curiosity and hypothesis‑driven thinking.

Nice to Have

• Experience collaborating with ML researchers on infrastructure for messy experimental workflows.
• Evidence of strong independent technical work—open‑source projects, competitions, or notable infrastructure contributions.
• Familiarity with veRL, SkyRL, Slime, FSDP, DeepSpeed, or similar distributed training frameworks.

Exclusions

• No hands‑on LLM or RL infrastructure experience.
• No meaningful GPU or distributed systems exposure.
• No evidence of strong technical ownership or independent work.

Compensation & Logistics

• $300,000 – $500,000 base (average offer $400K–$425K)
• Competitive equity at seed stage.
• Visa: H1B transfer, OPT, and O‑1 supported.
• San Francisco, on‑site.

Interview Process

1. Background, fit, and logistics.
2. Technical interview—deep dive on infrastructure experience, systems design, and RL/LLM stack.
3. On‑site loop—1–2 days with researchers and engineering team in San Francisco.