Software Engineer, River API
riverai
6 months ago
Full-time
On-site
Palo Alto, CA
$200,000 - $420,000 USD yearly
Engineering
Moneyboard classification: Published range reaches threshold
Reported USD annual base: $200,000–$420,000
Original source: Greenhouse
At River AI, 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.
Who we are
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 systems engineers to build the high-performance engines that train our models. Your goal is to make training at River fast, reliable, and massively scalable.
You will take ownership of our core infrastructure stack; from writing custom GPU kernels to managing clusters of thousands of nodes, ensuring our researchers can focus on science rather than system bottlenecks.
What You’ll Do
Architect and deploy fault-tolerant distributed systems for training and inference workloads across clusters with thousands of nodes.
Design high-performance kernels to maximize tensor operation efficiency, memory throughput, and networking over InfiniBand/RDMA.
Profile systems end-to-end to resolve blockers across hardware, software, data loading pipelines, and collective communication primitives.
Partner directly with research scientists to rapidly implement, optimize, and scale experimental model architectures.
Skills & Qualifications
Minimum Qualifications:
Bachelor’s degree in Computer Science, Computer Engineering, or equivalent practical industry experience.
Deep expertise in systems-level languages (C, C++, or Rust) with a track record of writing performant, maintainable code.
Strong foundation in computer architecture, memory management, and concurrent programming.
Exceptional debugging skills, especially when tackling complex, non-deterministic issues in distributed environments.
A highly collaborative mindset and a bias for action to push boundaries across the stack.
Preferred Qualifications:Â (We encourage you to apply even if you don't meet all of these)
Hands-on experience with modern AI frameworks (e.g., PyTorch, JAX) and tooling for large-scale model training.
Deep familiarity with modern GPU architectures (NVIDIA/AMD) and hardware constraints (HBM bandwidth, PCIe limits).
A proven track record of shipping and maintaining high-performance distributed systems or low-level software libraries.
Logistics & Benefits
Location:Â Palo Alto, California.
Compensation: Depending on experience and skills the expected base pay is $200,000 - $420,000 USD per year, plus equity.
Benefits:Â Comprehensive health, dental, and vision insurance; unlimited PTO; and relocation assistance as needed.
Visa Sponsorship:Â We sponsor visas and are committed to supporting the process for the right candidate.
Reported USD annual base: $200,000–$420,000
Original source: Greenhouse
At River AI, 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.
Who we are
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 systems engineers to build the high-performance engines that train our models. Your goal is to make training at River fast, reliable, and massively scalable.
You will take ownership of our core infrastructure stack; from writing custom GPU kernels to managing clusters of thousands of nodes, ensuring our researchers can focus on science rather than system bottlenecks.
What You’ll Do
Architect and deploy fault-tolerant distributed systems for training and inference workloads across clusters with thousands of nodes.
Design high-performance kernels to maximize tensor operation efficiency, memory throughput, and networking over InfiniBand/RDMA.
Profile systems end-to-end to resolve blockers across hardware, software, data loading pipelines, and collective communication primitives.
Partner directly with research scientists to rapidly implement, optimize, and scale experimental model architectures.
Skills & Qualifications
Minimum Qualifications:
Bachelor’s degree in Computer Science, Computer Engineering, or equivalent practical industry experience.
Deep expertise in systems-level languages (C, C++, or Rust) with a track record of writing performant, maintainable code.
Strong foundation in computer architecture, memory management, and concurrent programming.
Exceptional debugging skills, especially when tackling complex, non-deterministic issues in distributed environments.
A highly collaborative mindset and a bias for action to push boundaries across the stack.
Preferred Qualifications:Â (We encourage you to apply even if you don't meet all of these)
Hands-on experience with modern AI frameworks (e.g., PyTorch, JAX) and tooling for large-scale model training.
Deep familiarity with modern GPU architectures (NVIDIA/AMD) and hardware constraints (HBM bandwidth, PCIe limits).
A proven track record of shipping and maintaining high-performance distributed systems or low-level software libraries.
Logistics & Benefits
Location:Â Palo Alto, California.
Compensation: Depending on experience and skills the expected base pay is $200,000 - $420,000 USD per year, plus equity.
Benefits:Â Comprehensive health, dental, and vision insurance; unlimited PTO; and relocation assistance as needed.
Visa Sponsorship:Â We sponsor visas and are committed to supporting the process for the right candidate.