Member of Technical Staff, Research

River AI•Palo Alto, CA

2d•Onsite

About The Position

At River, our mission is to create personal AI owned and shaped by each individual. To achieve this, we are rewriting the entire stack from scratch: personal hardware for local inference, bespoke training infrastructure, next-generation UIs, and frontier deep learning research. We are scientists, engineers, and builders from the industry's top tech companies and AI labs. We bring a proven track record of scaling consumer systems for hundreds of millions of users and architecting the pre-training infrastructure behind today's frontier models. About the Role We are looking for exceptional researchers to design and train the foundation models that power River's personal AI. Your goal is to push the frontier of deep learning, focusing on architectures that can continuously learn, deeply personalize, and run efficiently on local hardware. You will take ownership of the research lifecycle from ideating novel algorithms to scaling large training runs, ensuring our AI evolves alongside the user to become a true extension of their will.

Requirements

BS, MS, or Ph.D. in Computer Science, Machine Learning, Mathematics, or equivalent practical industry experience.
Deep understanding of modern deep learning architectures (e.g., Transformers, diffusion models) and advanced training methodologies.
Extensive hands-on experience with PyTorch or JAX, with a track record of writing clean, scalable code for model training.
Proven ability to take open-ended research problems from mathematical formulation to working, scaled implementations.
A highly collaborative mindset and a bias for action to push boundaries in a fast-paced environment.

Nice To Haves

Track record of impactful publications at top-tier AI conferences (e.g., NeurIPS, ICLR, ICML) or equivalent industry research breakthroughs.
Specific expertise in continual learning, reinforcement learning, or optimizing models for edge/on-device inference.
Experience pre-training or aligning frontier-scale language or multimodal models.

Responsibilities

Design, train, and evaluate novel foundation models optimized for reasoning, multimodal understanding, and extreme personalization.
Pioneer research in continual learning, enabling models to adapt in real-time based on local user interaction without catastrophic forgetting.
Develop advanced techniques for data efficiency, alignment (e.g., RLHF), and parameter-efficient fine-tuning (PEFT) targeting our bespoke personal hardware.
Partner directly with the infrastructure team to rapidly scale and unblock experimental architectures across large compute clusters.