AI Theory & Systems, Algorithms & Models

Unconventional AI
Palo Alto
Workplace: OnsiteFull timeFunction: Data Science & Machine LearningEducation: phdSkills: []

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.

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FursaFursa
Unconventional AI
Unconventional AI
1 month ago

AI Theory & Systems, Algorithms & Models

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

Source: Company careers pageValidated by: Fursa AI
Last checked: 22 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
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