4 months ago

System Modeling (Computation)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Join a hands-on R&D team building GPU-accelerated, physics-based system models and ML simulations for unconventional compute. You will contribute to cross-layer design, develop high-fidelity solvers in PyTorch, and optimize ML workloads across hardware and software layers in a multidisciplinary group focused on energy-efficient AI computing.

Location: Palo Alto

Workplace: Remote

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Building large-scale, GPU-accelerated, high-fidelity differential equation solvers to enable ML on analog/unconventional hardware with PyTorch visualization
•Developing physics-based models of device- and system-level behavior in unconventional compute, integrated with PyTorch, to expose algorithm–hardware tradeoffs
•Collaborating with other teams to understand modeling needs for high-level algorithm development and lower-level hardware verification
•Designing and implementing ML workflows and surrogates for ODE/SDE/DDE solvers to accelerate cross-layer optimization
•Profiling, debugging, and optimizing large-scale ML systems to improve performance, stability, and scalability

Key Requirements

•MS/PhD in a quantitative field (AI/ML, Computer Science, Physics, Electrical Engineering, Applied Math), or BS with substantial, clear evidence of equivalent research/engineering depth.
•Dynamical computation knowledge
•Experience with high-performance, customized GPU kernel development for numerical differential equation solvers (e.g., ODE, SDE), covering skills for GPU memory optimization and scaling for multi-GPUs.
•Experience with DE solving algorithms designed for parallel architectures such as Rosenbrock methods, Euler-Maruyama method, high-order polynomial interpolations techniques.
•Experience with building effective neural network surrogate models for ODE/SDE/DDE solvers and functions

Education:PhD / Doctorate

Languages:English

Tech Stack:PythonPyTorchCUDATritonGPUGPU kernelJAXNumPyTensorFlowModalHPCMPINCCLSciPySDEODE

Company Brief

Unconventional AI

Builds customizable AI copilots and fine-tuned large language model solutions to help teams automate workflows, surface actionable insights, and integrate generative AI into business processes across products and operations.

Industry: AI & Machine Learning

Company Size: Small (11 to 50 employees)

Growth: Early Stage Startup

Funding: Seed

Website

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Unconventional AI

4 months ago

System Modeling (Computation)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Location: Palo Alto

Workplace: Remote

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Building large-scale, GPU-accelerated, high-fidelity differential equation solvers to enable ML on analog/unconventional hardware with PyTorch visualization
•Developing physics-based models of device- and system-level behavior in unconventional compute, integrated with PyTorch, to expose algorithm–hardware tradeoffs
•Collaborating with other teams to understand modeling needs for high-level algorithm development and lower-level hardware verification
•Designing and implementing ML workflows and surrogates for ODE/SDE/DDE solvers to accelerate cross-layer optimization
•Profiling, debugging, and optimizing large-scale ML systems to improve performance, stability, and scalability

Key Requirements

•MS/PhD in a quantitative field (AI/ML, Computer Science, Physics, Electrical Engineering, Applied Math), or BS with substantial, clear evidence of equivalent research/engineering depth.
•Dynamical computation knowledge
•Experience with high-performance, customized GPU kernel development for numerical differential equation solvers (e.g., ODE, SDE), covering skills for GPU memory optimization and scaling for multi-GPUs.
•Experience with DE solving algorithms designed for parallel architectures such as Rosenbrock methods, Euler-Maruyama method, high-order polynomial interpolations techniques.
•Experience with building effective neural network surrogate models for ODE/SDE/DDE solvers and functions

Education:PhD / Doctorate

Languages:English

Tech Stack:PythonPyTorchCUDATritonGPUGPU kernelJAXNumPyTensorFlowModalHPCMPINCCLSciPySDEODE

Company Brief

Unconventional AI

Industry: AI & Machine Learning

Company Size: Small (11 to 50 employees)

Growth: Early Stage Startup

Funding: Seed

Website