Unconventional AI

6 days ago

AI Theory & Systems, Algorithms & Models

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 16 hours agoStatus: Live

Job Summary

Design and scale next-generation AI model architectures that blend novel computational primitives with conventional neural network components. Co-design diffusion, flow, energy-based models, Neural ODEs, and Deep Equilibrium Models, then establish scaling laws tailored to novel hardware. Explore architectures across image generation, video, language & reasoning, and world models, and translate theoretical insights and physical constraints into actionable specifications for systems and hardware teams.

Location: Palo Alto

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Co-design and evaluate next-generation AI models, implementing core modeling components across both conventional and unconventional architectures.
•Establish and test scaling laws specific to novel hardware, and develop/refine methods (e.g., muP) to push model capability forward.
•Explore novel architectures across modalities including image generation, video, language & reasoning, and world models; develop general learning frameworks (e.g., flow matching).
•Collaborate across the organization as a translator between theoretical insights, physical hardware constraints, and concrete system/hardware specifications.

Pay and Benefits

Perks:Health Insurance401kPaid LeaveMeal Allowance

Key Requirements

•MS/PhD (or equivalent research/project experience) in a quantitative field such as AI/ML, Computer Science, Physics, Electrical Engineering, or Applied Math.
•Deep expertise in the theory, training, and empirical analysis of modern ML model architectures, with demonstrated ability to design, implement, and scale models across tasks and modalities.
•Strong understanding of both conventional architectures (Transformers, Mixture of Experts, diffusion models) and continuous-time/implicit architectures (Neural ODEs, Deep Equilibrium Models, flow-based generative models).
•Deep experience with PyTorch or JAX, including implementing custom model components and training loops.

Education:PhD / Doctorate

Languages:English

Tech Stack:PyTorchJAXTransformersMixture of ExpertsDiffusion modelsEnergy-based modelsNeural ODEsDeep Equilibrium ModelsFlow-based generative modelsFlow matchingMuPAttentionNormalizationFFNs

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

6 days ago

AI Theory & Systems, Algorithms & Models

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 16 hours agoStatus: Live

Job Summary

Location: Palo Alto

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Co-design and evaluate next-generation AI models, implementing core modeling components across both conventional and unconventional architectures.
•Establish and test scaling laws specific to novel hardware, and develop/refine methods (e.g., muP) to push model capability forward.
•Explore novel architectures across modalities including image generation, video, language & reasoning, and world models; develop general learning frameworks (e.g., flow matching).
•Collaborate across the organization as a translator between theoretical insights, physical hardware constraints, and concrete system/hardware specifications.

Pay and Benefits

Perks:Health Insurance401kPaid LeaveMeal Allowance

Key Requirements

•MS/PhD (or equivalent research/project experience) in a quantitative field such as AI/ML, Computer Science, Physics, Electrical Engineering, or Applied Math.
•Deep expertise in the theory, training, and empirical analysis of modern ML model architectures, with demonstrated ability to design, implement, and scale models across tasks and modalities.
•Strong understanding of both conventional architectures (Transformers, Mixture of Experts, diffusion models) and continuous-time/implicit architectures (Neural ODEs, Deep Equilibrium Models, flow-based generative models).
•Deep experience with PyTorch or JAX, including implementing custom model components and training loops.

Education:PhD / Doctorate

Languages:English

Tech Stack:PyTorchJAXTransformersMixture of ExpertsDiffusion modelsEnergy-based modelsNeural ODEsDeep Equilibrium ModelsFlow-based generative modelsFlow matchingMuPAttentionNormalizationFFNs

Company Brief

Unconventional AI

Industry: AI & Machine Learning

Company Size: Small (11 to 50 employees)

Growth: Early Stage Startup

Funding: Seed

Website