4 months ago

System Modeling (Dynamic Systems Simulation)

✓ 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 developing physics-based system models and GPU-accelerated ML simulations for unconventional physics-based computing. You’ll build high-performance PyTorch components to model dynamic systems, contributing to cross-layer integration and next-generation AI architectures in a multi-disciplinary environment.

Location: Palo Alto

Workplace: Hybrid

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Develop high-performance PyTorch components that model complex, time-varying dynamic systems.
•Contribute to cross-layer system integration and GPU-accelerated ML system simulations.
•Engage in design and debugging of ML models, profiling and stabilizing large-scale ML systems.
•Collaborate across physics-based modeling, software, and hardware concepts to ground digital models in physical reality.
•Help advance
•n/a

Pay and Benefits

Perks:Health Insurance401kPaid Leave

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 systems simulation knowledge
•Advanced Neural Modeling (PyTorch): Deep proficiency in PyTorch, specifically in building custom autograd functions and integrating numerical solvers (e.g., Neural ODEs) to represent dynamic processes.
•Dynamics & Differential Equations: Strong grasp of linear and non-linear dynamics, state-space representations, and solving dx/dt = f(x, u, t) in ML context.
•Stochastic Processes & Noise: Understanding how to model and mitigate noise in real-world systems, including experience with SDEs or Bayesian filtering.

Experience:AIMLSimulation

Education:Master's

Skills:PyTorchCUDATritonGPU programmingJAXNumPyTensorFlowMPINCCLDistributed trainingSciPy

Languages:English

Tech Stack:PyTorchCUDATritonGPU programmingJAXNumPyTensorFlowModalMPINCCLSciPyTorch.compileDistributed training

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 (Dynamic Systems Simulation)

✓ 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: Hybrid

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Develop high-performance PyTorch components that model complex, time-varying dynamic systems.
•Contribute to cross-layer system integration and GPU-accelerated ML system simulations.
•Engage in design and debugging of ML models, profiling and stabilizing large-scale ML systems.
•Collaborate across physics-based modeling, software, and hardware concepts to ground digital models in physical reality.
•Help advance
•n/a

Pay and Benefits

Perks:Health Insurance401kPaid Leave

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 systems simulation knowledge
•Advanced Neural Modeling (PyTorch): Deep proficiency in PyTorch, specifically in building custom autograd functions and integrating numerical solvers (e.g., Neural ODEs) to represent dynamic processes.
•Dynamics & Differential Equations: Strong grasp of linear and non-linear dynamics, state-space representations, and solving dx/dt = f(x, u, t) in ML context.
•Stochastic Processes & Noise: Understanding how to model and mitigate noise in real-world systems, including experience with SDEs or Bayesian filtering.

Experience:AIMLSimulation

Education:Master's

Skills:PyTorchCUDATritonGPU programmingJAXNumPyTensorFlowMPINCCLDistributed trainingSciPy

Languages:English

Tech Stack:PyTorchCUDATritonGPU programmingJAXNumPyTensorFlowModalMPINCCLSciPyTorch.compileDistributed training

Company Brief

Unconventional AI

Industry: AI & Machine Learning

Company Size: Small (11 to 50 employees)

Growth: Early Stage Startup

Funding: Seed

Website