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River AI

API for fine-tuning open-weight models

Design Verification Emulation - Hardware

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

About the Job

Requirements

  • Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, and 5+ years of industry experience.
  • Hands-on experience with emulation platforms such as Cadence Palladium/Protium, Synopsys ZeBu/HAPS, or comparable systems, including bring-up, model integration, compilation, execution, and debug.
  • Experience building or adapting models and test environments for simulation acceleration, emulator-hosted verification, or both.
  • Strong understanding of verification planning, regression strategy, and verification closure, including translating DV goals into emulation workload and run criteria.
  • Experience verifying complex IP or SoCs and understanding verification needs from block-level to system-level testing.
  • Proficiency in SystemVerilog, C/C++, and scripting languages such as Python, Tcl, or Bash.
  • Ability to assess effective tests and workloads for emulation platforms, including tradeoffs among runtime, fidelity, observability, and debug.
  • Ability to collaborate across design, verification, software, and infrastructure teams.

Responsibilities

  • Translate IP and SoC verification plans into execution strategies, mapping tests to simulation, simulation acceleration, or emulator-hosted verification based on runtime, fidelity, and debug needs.
  • Define emulation workload goals and run acceptance criteria with DV block owners.
  • Build and maintain models and test components for simulation acceleration and emulator-hosted verification flows, balancing fidelity, performance, and debug visibility.
  • Develop and improve platform bring-up, compile, mapping, runtime, regression, and debug flows.
  • Adapt or develop test environments, transactors, stimulus, and software-driven workloads for emulation platforms.
  • Track workload execution and failures, review results with DV block owners, and help close emulation-specific gaps; provide emulation signoff as an input to overall IP and SoC verification signoff.
  • Triage emulation compile, runtime, and test failures; use traces, waveforms, and platform diagnostics to isolate design, model, testbench, or infrastructure issues; partner with RTL and DV block owners to resolve them.
  • Build and apply agentic workflows across workload planning, model and test preparation, platform setup, run orchestration, failure triage, result analysis, and closure reporting, keeping results traceable and reviewable by DV owners.
  • Build and maintain scripts and tools using Python, Tcl, Bash, or similar languages to improve platform usability, regression throughput, and results visibility.

Desired Qualifications

  • Experience using emulation for long-running software workloads, firmware and driver validation, system-level scenarios, or pre-silicon software bring-up.
  • Background developing synthesizable test components, transactors, behavioral models, or emulation-friendly replacements for simulation models.
  • Experience with UVM-based verification environments and adapting them for acceleration or emulation.
  • Familiarity with coverage-driven verification and SystemVerilog Assertions (SVA).
  • Experience using emulation workloads for performance verification and power estimation.
  • Experience with high-performance silicon such as CPUs, GPUs, AI accelerators, or complex SoCs.
  • Experience with workflow management, CI/CD, regression definition and visibility, EDA 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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