Member of Technical Staff, RL Infra

inception; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

Inception is seeking engineers and scientists to design, optimize, and maintain core systems enabling scalable reinforcement learning for large models. You will work at the intersection of research and systems engineering, optimizing rollout and reward pipelines, improving reliability, observability, and orchestration, and ensuring production-readiness.

Responsibilities include building infrastructure for RL workloads, boosting training throughput, and creating shared tooling for monitoring and

Qualifications

• Understanding of RL systems from a systems perspective.
• Experience with large-scale ML workflows and orchestration.
• Familiarity with reliability, observability, and production readiness.

Responsibilities

• Design, build, and optimize infrastructure for large-scale RL workloads.
• Improve reliability and throughput of RL training pipelines.
• Develop monitoring and observability tools for high uptime and debuggability.

Skills

RL systems

Distributed systems

Observability

Orchestration

Education

BS/MS/PhD in CS/Engineering

Tools

Docker

Kubernetes

CI/CD

Job description

Overview

We're looking for engineers and scientists to design, optimize, and maintain the core systems that enable scalable, efficient reinforcement learning for large models. This role sits at the intersection of research and large-scale systems engineering: you\'ll wear many hats, from optimizing rollout and reward pipelines to enhancing reliability, observability, and orchestration, collaborating closely with researchers to make RL stable, fast, and production-ready.

Responsibilities

• Design, build, and optimize the infrastructure that powers large-scale reinforcement learning and post-training workloads.
• Improve the reliability and scalability of RL training pipelines, distributed RL workloads, and training throughput.
• Develop shared monitoring and observability tools to ensure high uptime, debuggability, and reproducibility for RL systems.

Qualifications

• BS/MS/PhD in Computer Science, Engineering, or a related field (or equivalent experience).
• Understanding of ML frameworks (PyTorch, TensorFlow, Ray, Megatron) from a systems perspective.
• Experience working with reinforcement learning workloads (PPO, DPO, RLHF, or reward modeling).
• Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.

Preferred Skills

• Experience building and maintaining large-scale language models with tens of billions of parameters or more.
• Experience with ML workflow orchestration tools (Kubeflow, Airflow).
• Background in performance optimization and profiling of ML systems.