Research Engineer - Post-Training

The role

You will build the RL post-training stack end-to-end, including rollout ingestion, reward computation, and policy updates. You will adapt RL algorithms for asynchronous, high-latency, and partially trusted generation on consumer GPUs and Macs.

What you will do

• Build the RL training loop end-to-end
• Handle rollout ingestion from the geo-distributed inference pipeline
• Perform reward computation and policy updates
• Get updated weights back out to the network
• Adapt RL recipes for asynchronous, high-latency, and partially trusted generation
• Develop staleness tolerance, off-policy corrections, and communication-efficient policy updates
• Build evaluations to show model improvement
• Take the first decentralized post-trained release from run to public artifact

Required qualifications

• Hands-on RL post-training on large language models (RLHF, RLVR, or reasoning-focused RL)
• Experience with the systems layer including rollout generation, async training loops, and weight synchronization
• Production-quality Python and PyTorch
• Experience with concurrency, failure handling, and profiling
• Research ability in RL post-training, asynchronous or distributed RL, or nearby fields
• Professional-level English proficiency (written and spoken)

Nice to have

• Experience training over slow networks, or with decentralized or federated setups
• Familiarity with serving-engine internals such as vLLM or SGLang
• Experience with reward modeling or building verifiable-reward datasets
• Experience with P2P networking and NAT traversal
• Experience at proprietary, open-weight and open-source AI labs

Benefits

• Equity-Heavy Package including significant ownership and a high base salary
• Remote-First Culture
• Optional full visa sponsorship and relocation support to Australia or the US

Before you apply

• Must be comfortable working across timezones

About the company

Pluralis Research works on Protocol Learning to train and serve large models in a fully decentralized way on small consumer-grade devices. They are a world-class, deeply technical team of ML researchers backed by Union Square Ventures.