Research Scientist - Pre-training

OpenKrill; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity-Heavy Package

Remote-First Culture

Visa Sponsorship & Relocation support

Open Problems: frontier model research

Job summary

Pluralis Research seeks a Research Scientist to build and scale training systems for Protocol Learning across heterogeneous hardware and regions, connected by ordinary internet. You will tackle distributed pretraining, fault tolerance, and performance optimizations while advancing frontier models.

We value hands-on experience with large-scale distributed setups, production-grade Python, and evidence of open-source or research-oriented projects.

Qualifications

• Hands-on distributed training on many devices using PyTorch with model-parallelism.
• Strong engineering practices in Python, concurrency and failure handling.
• Evidence of shipped systems or open-source projects is a plus.

Responsibilities

• Distributed pretraining: implement and optimize model-parallel training.
• Performance optimization to reduce communication overhead.
• Elasticity and fault tolerance for churn and node failures.
• Run instrumentation to monitor throughput and model quality.

Skills

Distributed training across devices

PyTorch

Model parallelism

Production-quality Python

Tools

FSDP / DeepSpeed / Megatron

Job description

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 https://arxiv.org/abs/2607.13332). 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 https://pluralis.ai/blog/a-third-path-protocol-learning/.

This setting breaks nearly every assumption of datacenter training: communication-efficient training across different parallelism axes, fault tolerance as nodes join and drop mid-run, heterogeneous compute and networks, and robustness to malicious participants. Our published methods include Subspace Networks https://arxiv.org/abs/2506.01260, Factored Gossip DiLoCo https://arxiv.org/abs/2606.22768, AsyncMesh https://arxiv.org/abs/2601.22442, and Sentinel https://arxiv.org/abs/2603.03592.

As a Research Scientist you'll build the training system that takes Protocol Learning from the 8B run to frontier scale: large models on heterogeneous hardware, in physically different regions, connected by ordinary internet.

KEY RESPONSIBILITIES

• Distributed pretraining: Implement and optimize model-parallel training. Data, pipeline, and tensor parallelism for large models on heterogeneous GPUs under low-bandwidth, high-latency links.
• Performance optimization: Implement techniques that reduce communication overhead while maintaining model convergence in challenging network environments.
• Elasticity and fault tolerance: Make runs survive node churn. Robust checkpointing, state synchronization, and recovery as participants join and leave.
• Run instrumentation: Build the monitoring that shows throughput, bottlenecks, and model quality across hundreds of devices.

WHAT WE'RE LOOKING FOR

• Hands-on distributed training (required): You've trained models across many devices in PyTorch with FSDP, DeepSpeed, Megatron, or your own implementation. You understand data, tensor, and pipeline parallelism.
• Strong engineering: Production-quality Python. Concurrency, failure handling, profiling before optimizing.
• Evidence of execution: Shipped systems, research code, open-source work, or serious personal projects.
• Mission alignment: You believe Protocol Learning is the viable third path for collective, trustless, and sovereign AI.

NICE TO HAVE

• Hands-on experience training or serving large language models such as Nemotron, Qwen or OLMo.
• Experience with P2P networking and NAT traversal.
• Experience with post-training and RL.
• Experience with inference and serving systems.
• 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 https://www.usv.com/ 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.