Distributed Training Engineer — High-Perf GPU Scale

River AI; Palo Alto (CA); On-site; USD 200,000 - 420,000; Full time

Benefits offered by this job

Health insurance

Relocation assistance

Visa sponsorship

Job summary

River AI in Palo Alto, CA, seeks exceptional systems engineers to build the distributed training engines behind the River API. Your work will focus on making fine-tuning fast, numerically correct, and reliable across large GPU clusters.

You will own execution of training workloads, including gradient computation, optimizer updates, rollout coordination, and checkpoint recovery. Collaborate with researchers and inference engineers to deploy new learning methods and improve compute efficiency.

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, or equivalent practical experience.
• Hands-on experience building or improving distributed model-training systems.
• Strong proficiency in Python and a modern deep-learning framework such as PyTorch or JAX.
• Solid understanding of backpropagation, optimizers, mixed-precision training, and GPU memory management.
• Strong debugging skills across concurrent execution and distributed failure recovery.

Responsibilities

• Build and optimize distributed training for large dense and mixture-of-experts models.
• Improve reinforcement-learning pipelines by coordinating sampling, reward computation, training updates, and weight transfer.
• Optimize GPU memory use, parallelism, and communication to increase training throughput.
• Implement reliable checkpointing, resumption, and worker recovery while preserving training state.
• Validate losses, gradients, and optimizer behavior, and diagnose numerical or distributed execution failures.
• Partner with researchers to implement new algorithms and make them accessible through the River API.

Skills

Distributed training

Python

PyTorch

JAX

Backpropagation

GPU memory management

Distributed failure recovery

Education

Bachelors in CS/CE

Tools

NCCL

CUDA

C++

Rust

Job description

River AI in Palo Alto, CA, seeks exceptional systems engineers to build the distributed training engines behind the River API. Your work will focus on making fine-tuning fast, numerically correct, and reliable across large GPU clusters.

You will own execution of training workloads, including gradient computation, optimizer updates, rollout coordination, and checkpoint recovery. Collaborate with researchers and inference engineers to deploy new learning methods and improve compute efficiency.