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River AI

API for fine-tuning open-weight models

Design Verification Engineer - Hardware

Hardware Validation & TestingConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A bachelor's degree in Electrical Engineering, Computer Engineering, or Computer Science, and 5 or more years of industry experience.
  • Hands-on experience building Universal Verification Methodology (UVM)-based environments, including agents, scoreboards, drivers, monitors, and predictors.
  • Command of SystemVerilog for verification, including object-oriented programming principles, randomization, and functional coverage.
  • Understanding of computer architecture, including pipelines, cache coherency protocols, memory consistency models, and high-speed interconnects such as AXI and CHI.
  • A track record of verifying high-performance silicon, such as CPUs, GPUs, AI accelerators, or complex systems-on-chip.
  • A collaborative mindset and bias for action to push boundaries and co-design effectively with other engineers.

Responsibilities

  • Define comprehensive verification plans by extracting features from architectural and micro-architectural specifications for complex blocks.
  • Architect and implement scalable, reusable, and robust testbenches using SystemVerilog and Universal Verification Methodology (UVM).
  • Develop complex constrained-random sequences and test cases to achieve high functional coverage and verify architectural corner cases and error conditions.
  • Define and implement functional coverage models and perform detailed code coverage analysis to quantify verification progress.
  • Use formal methods, including SystemVerilog Assertions and property checking, where applicable to prove critical logic correctness.
  • Use emulation and prototyping platforms such as ZeBu, HAPS, Palladium, and Protium for scaled testing.
  • Debug failures using waveform viewers and work with RTL designers to resolve functional bugs and architectural non-compliance.
  • Build and maintain regression flows and productivity tools using Python, Perl, Bash, or Tcl to streamline the verification cycle.

Desired Qualifications

  • Proficiency in SystemVerilog Assertions and formal tools such as Jasper and VC Formal to catch temporal logic bugs at the source.
  • Understanding of emulation and prototyping solutions and test environments.
  • Hands-on experience in post-silicon bring-up, testing, and debugging.
  • Experience in workflow management, continuous integration and continuous delivery, regression definition and visibility, electronic design automation tool configuration and usage, and other infrastructure tasks.

About the Company

River AI

API for fine-tuning open-weight models

River AI helps developers and enterprises train, fine-tune, and own custom open-weight AI models using its River API, which focuses on open-weight models rather than a single lab-owned general model. The API supports LoRA fine-tuning and reinforcement learning on frontier open weights, featuring fast weight transfers, sampling-training consistency, elastic compute, and token-based billing that avoids idle GPU charges. It differentiates itself by enabling true ownership and control of models, offering a full-stack solution for training, fine-tuning, and deploying custom models without relying on a centralized general model. Its goal is to empower individuals and organizations to own, customize, and run their own AI systems with close-to-the-user control.

Company size
11-50
Company stage
Early VC
Total funding
$1.1B
Headquarters
Palo Alto, CA
Founded
2026

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