Research Scientist - Post-Training

pluralis-research; United States; Remoto; USD 150.000 - 210.000; Tempo pieno

Vantaggi offerti da questo lavoro

Equity-Heavy Package

Remote-First Culture

Visa Sponsorship & Relocation

Open Problems

Descrizione del lavoro

Pluralis Research is pioneering Protocol Learning, training and serving large models in a decentralized way across consumer hardware. We are hiring for hands-on RL post-training and delivery of first decentralized releases to the network.

In this role you will build end-to-end RL pipelines, optimize asynchronous training, and ensure weight synchronization across geo-distributed systems, while contributing to publications and system design for scalable serving.

Competenze

• Hands-on RL post-training on large language models and systems work.
• Strong production-quality Python and PyTorch engineering.

Mansioni

• Build the post-training RL training loop end-to-end: rollout ingestion, reward computation, policy updates, and weight propagation.
• Invent algorithms for asynchronous, high-latency, partially trusted generation with staleness tolerance and off-policy corrections.
• Ship first post-trained models and release public artifacts with evaluation pipelines.

Conoscenze

RL post-training

Python programming

PyTorch

Distributed training

Async training

Weight synchronization

Strumenti

Python

PyTorch

Distributed training frameworks

RL libraries

Descrizione del lavoro

Pluralis Research works on Protocol Learning: training and serving large models in a fully decentralized way on small consumer-grade devices connected via the internet. Despite being dismissed as infeasible, we have made significant advances on this problem, most recently Agora, a permissionless run that pretrained an 8B model from scratch on consumer GPUs spread over the internet, with no single participant ever holding the full weights ( tech report ). While many of the core research problems have been solved, Protocol Learning unlocks a series of new challenges. For the mission in full, read A Third Path: Protocol Learning.

Key Responsibilities

• Build the post-training stack: You build the RL training loop end-to-end: rollout ingestion from the geo-distributed inference pipeline, reward computation, policy updates, and getting updated weights back out to the network. You set the direction, and you make things happen.
• Invent the algorithms: Standard RL recipes assume on-policy rollouts from fast, trusted hardware. You adapt them to asynchronous, high-latency, partially trusted generation: staleness tolerance, off‑policy corrections, and communication‑efficient policy updates.
• Ship first post‑trained models: You build the evals that show the models are improving, and you take the first decentralized post‑trained release from run to public artifact.

What We're Looking For

• Hands‑on RL post‑training: You've run RL post‑training on large language models – RLHF, RLVR, or reasoning‑focused RL – and touched the systems layer yourself: rollout generation, async training loops, weight synchronization. Not just launched jobs on someone else’s stack.
• Strong engineering: Production‑quality Python and PyTorch: concurrency, failure handling, profiling before optimizing.
• Research ability: Publications in RL post‑training, asynchronous or distributed RL, or nearby fields are a strong signal. So is unpublished work you can defend in detail.
• Mission alignment: You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI.

Nice to Have

• Experience training over slow networks, or with decentralized or federated setups.
• Familiarity with serving‑engine internals such as vLLM or SGLang – our rollout pipeline is a serving system.
• 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.

Compensation & Benefits

• Equity‑Heavy Package: We offer significant ownership for key technical contributors in addition to a high base salary.
• Remote‑First Culture: Flexible work environment with team members distributed globally.
• Visa Sponsorship: Optional full visa sponsorship and relocation support to either Australia or the US.
• Open Problems: Training and serving frontier models on hardware you don't control, over networks you don't own, mostly has no published answers yet. You'll write some of the first ones.

FYI's

• We work remotely across the world, with the main teams in Australia and North America. You'll need to be comfortable working across timezones.
• Applicants must have professional‑level English proficiency (written and spoken).

We are backed by Union Square Ventures and other tier‑1 investors, and we are a world‑class, deeply technical team of ML researchers. Pluralis is unapologetically ideological. We believe AI, and the world, end up on a better path if we succeed in implementing the protocol for intelligence.