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Prime Intellect
Prime Intellect
6 open remote positions
Prime Intellect is building an open superintelligence stack, providing the infrastructure and platform for frontier AI training and deployment. The company has raised $150M from top investors and boasts a mission-driven team working to decentralize AI.
Salary Distribution
6 of 6 jobs
Benefits Overview
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Open Positions
Build and extend pretraining code to scale experiments into full training runs. Evaluate model architecture changes and optimize training throughput and resilience. Run controlled ablations and collaborate with data and evaluation researchers.
Python
PyTorch
Distributed Training
Machine Learning
Design and build challenging evaluations and executable environments for coding and long-horizon agents. Develop scoring logic and verifiers to detect reward hacks and trace failures through model outputs and harness code. Build and scale RL data pipelines for generating and curating challenging coding and agentic tasks.
Python
Data Pipelines
Reinforcement Learning
Build inference systems powering RL research, scaling toward 1–3T parameter models on NVL72 clusters. Optimize rollout throughput, multi-node MoE inference, and weight-update overhead for long-horizon agents. Collaborate with model researchers on architecture choices balancing model quality and execution cost.
Python
PyTorch
VLLM
CUDA
Find, evaluate, and collect valuable training data from web, code, and technical sources. Build scalable pipelines that turn raw data into training-ready corpora through extraction, cleaning, deduplication, and filtering. Work with researchers on data mixtures and sampling strategies for pretraining and midtraining.
Python
Data Engineering
Distributed Systems
Machine Learning
Develop RL algorithms for reasoning, coding, and long-horizon tasks. Design and run scaling experiments to improve training recipes across model sizes. Extend RL training frameworks for asynchronous workflows and reliability.
Python
PyTorch
Reinforcement Learning
Distributed Systems
Research
Build and optimize RL infrastructure for large-scale training. Improve efficiency across compute, memory, networking, and scheduling. Design low-level optimizations, from kernels to runtime.
PyTorch
Distributed Training
CUDA
Performance Optimization