AI Systems
Palo Alto, CA · On-site
$123K - $168K/yr
Required : • An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math. • Deep ...
AI Systems
Palo Alto, CA · On-site
$123K - $168K/yr
Required : • An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math. • Deep ...
AI Systems, Training
Palo Alto, CA · On-site
$123K - $168K/yr
An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math. • Experience: Veteran of ...
AI Systems, Training
Palo Alto, CA · On-site
$123K - $168K/yr
An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math. • Experience: Veteran of ...
AI Engineer, Multimodal LLMs
San Francisco, CA · On-site
$93K - $124K/yr
Meet Eloquent AI At Eloquent AI, we're building the next generation of AI Operators-multimodal ... Solid mathematical foundation of machine learning and deep learning techniques Bonus Points If.
AI Engineer, Multimodal LLMs
San Francisco, CA · On-site
$93K - $124K/yr
Meet Eloquent AI At Eloquent AI, we're building the next generation of AI Operators-multimodal ... Solid mathematical foundation of machine learning and deep learning techniques Bonus Points If.
Senior Math Libraries Engineer - AI and HPC
Santa Clara, CA · On-site
$122K - $168K/yr
NVIDIA Math Libraries team is looking for a senior engineer to join our development efforts in the area of kernel generation for AI and HPC, specifically targeting matrix operations, JITing and ...
Senior Math Libraries Engineer - AI and HPC
Santa Clara, CA · On-site
$122K - $168K/yr
NVIDIA Math Libraries team is looking for a senior engineer to join our development efforts in the area of kernel generation for AI and HPC, specifically targeting matrix operations, JITing and ...
Senior Math Libraries Engineer - AI and HPC
Santa Clara, CA · On-site
$122K - $168K/yr
NVIDIA Math Libraries team is looking for a senior engineer to join our development efforts in the area of kernel generation for AI and HPC, specifically targeting matrix operations, JITing and ...
Senior Math Libraries Engineer - AI and HPC
Santa Clara, CA · On-site
$122K - $168K/yr
NVIDIA Math Libraries team is looking for a senior engineer to join our development efforts in the area of kernel generation for AI and HPC, specifically targeting matrix operations, JITing and ...
AI Engineer - Fury Team
Sunnyvale, CA · On-site
... Mathematics, Physics, or related technical field, or equivalent practical experience. Advanced graduate research in AI, robotics, or machine learning is a plus. • Must be a U.S. Person due to ...
AI Engineer - Fury Team
Sunnyvale, CA · On-site
... Mathematics, Physics, or related technical field, or equivalent practical experience. Advanced graduate research in AI, robotics, or machine learning is a plus. • Must be a U.S. Person due to ...
Applied Researcher II
San Jose, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications * PhD in Computer ...
Applied Researcher II
San Jose, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications * PhD in Computer ...
Applied Researcher II
San Jose, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
Applied Researcher II
San Jose, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
Applied Researcher II
San Francisco, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications [choose correct set ...
Applied Researcher II
San Francisco, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications [choose correct set ...
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications [choose correct set ...
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications [choose correct set ...
Applied Researcher I
San Jose, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
Applied Researcher I
San Jose, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
Applied Researcher I
San Jose, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research Preferred Qualifications * PhD in Computer ...
Applied Researcher I
San Jose, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research Preferred Qualifications * PhD in Computer ...
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
Applied Researcher II
San Jose, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
Applied Researcher II
San Jose, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math. • Experience: Deep ...
An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math. • Experience: Deep ...
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research Preferred Qualifications [choose correct set ...
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research Preferred Qualifications [choose correct set ...
Applied Researcher II
San Francisco, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
Applied Researcher II
San Francisco, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
Applied Researcher II
San Francisco, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
Applied Researcher II
San Francisco, CA · On-site
S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: * PhD in Computer ...
Ai Math information
What is the difference between Ai Math vs Data Analyst?
| Aspect | Ai Math | Data Analyst |
|---|---|---|
| Required Credentials | Mathematics, Computer Science, AI certifications | Statistics, Data Analysis, Business Intelligence certifications |
| Work Environment | Research labs, AI development teams, tech companies | Business settings, consulting firms, corporate departments |
| Industry Usage | AI development, machine learning projects, research | Data interpretation, reporting, decision support |
Ai Math professionals focus on developing algorithms and models using advanced mathematics and AI techniques, often working in research or tech environments. Data Analysts interpret data to provide insights and support business decisions. While both roles require analytical skills, Ai Math emphasizes algorithm creation and AI research, whereas Data Analysts focus on data visualization and reporting.
What is an AI Math specialist?
An AI Math specialist is a professional who applies advanced mathematical concepts and techniques to develop, analyze, and improve artificial intelligence algorithms and models. Their work often involves linear algebra, calculus, probability, statistics, and optimization methods to design effective machine learning and deep learning systems. AI Math specialists collaborate with data scientists, engineers, and researchers to solve complex problems, ensure model accuracy, and enhance the performance of AI-driven solutions.
How does an AI Math specialist typically collaborate with data scientists and software engineers within a project team?
AI Math specialists play a crucial role in multidisciplinary teams by developing mathematical models and algorithms that underpin AI solutions. They frequently work alongside data scientists to refine statistical methods, validate results, and optimize data processing techniques. Collaboration with software engineers is also common, as AI Math specialists help translate theoretical models into efficient, scalable code for production environments. This teamwork ensures that AI systems are both mathematically sound and technically robust, fostering innovation and effective problem-solving.
What are the key skills and qualifications needed to thrive as an AI Math Specialist, and why are they important?
To thrive as an AI Math Specialist, you need strong mathematical foundations in linear algebra, calculus, probability, and statistics, typically supported by a degree in mathematics, computer science, or a related field. Proficiency with programming languages like Python, experience with machine learning frameworks (such as TensorFlow or PyTorch), and familiarity with data analysis tools are essential. Critical thinking, problem-solving, and effective collaboration are important soft skills for tackling complex challenges and working in interdisciplinary teams. These skills enable the development, implementation, and optimization of robust AI models and solutions.
What are popular job titles related to Ai Math jobs in California? For Ai Math jobs in California, the most frequently searched job titles are:
What job categories do people searching Ai Math jobs in California look for? The top searched job categories for Ai Math jobs in California are:
What cities in California are hiring for Ai Math jobs? Cities in California with the most Ai Math job openings:
$123K - $168K/yr
Full-time
Posted 7 days ago
Job description
Job Summary:
Unconventional AI is a company focused on revolutionizing computing to meet the unprecedented demand for energy-efficient AI solutions. As a Member of Technical Staff in AI Systems, you will develop architectural components and optimize them for physical silicon, bridging the gap between model architecture and hardware requirements.
Responsibilities:
• AI Architectural Modeling: Co-design and evaluate next-generation AI models (e.g, transformers, diffusion, flow, and energy-based models). You will collaborate closely across the team to combine, modify, and implement core modeling components, including both conventional (e.g., attention, normalization, Mixture-of-Experts, FFNs) and unconventional components. You will ensure that they function optimally across our novel compute substrates.
• Performance Modeling & Scaling: Establish and test scaling laws specific to our novel hardware. Develop rigorous performance models to evaluate compute vs. memory trade-offs
• Advanced Mapping & Partitioning: Drive the partitioning and mapping of complex AI models down to hardware. Apply and invent advanced optimization strategies from first principles, including custom quantization schemes, sparsity/pruning, and distillation to fit the physical constraints of our substrates.
• GPU Optimization & Kernel Development: Develop and optimize GPU kernels using low-level programming models like CUDA, Triton, or CUTLASS. Profile and debug complex ML codebases to resolve performance bottlenecks (training and inference).
• Cross-Functional Collaboration: Act as a translator, discussing algorithmic trade-offs with theorists and converting model requirements into concrete specifications for infrastructure and hardware engineering teams.
Qualifications:
Required:
• An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math.
• Deep, practical understanding of the modern AI/ML stack and optimized compilation and execution of algorithms on modern GPU systems.
• Proven experience in profiling, identifying, and resolving performance bottlenecks in complex ML codebases.
• Demonstrated ability to map state-of-the-art AI model architectures (e.g., Transformers, Mixture of Experts, diffusion models) to system performance implications and apply advanced efficiency techniques such as sparsity, quantization, and distillation.
• Deep experience with PyTorch, including its internals, torch.compile, and distributed data parallel (DDP) / fully sharded data parallel (FSDP) libraries.
Preferred:
• A forward-looking perspective on co-designing algorithms for unconventional computing paradigms that map closely to the physics of underlying systems.
• Theoretical or research experience in advanced approximation/compression techniques beyond standard quantization.
Company:
Unconventional AI rethinks computer foundations to optimize energy efficiency for AI. Founded in 2025, the company is headquartered in San Francisco, USA, with a team of 11-50 employees. The company is currently Early Stage.
Unconventional AI is a company focused on revolutionizing computing to meet the unprecedented demand for energy-efficient AI solutions. As a Member of Technical Staff in AI Systems, you will develop architectural components and optimize them for physical silicon, bridging the gap between model architecture and hardware requirements.
Responsibilities:
• AI Architectural Modeling: Co-design and evaluate next-generation AI models (e.g, transformers, diffusion, flow, and energy-based models). You will collaborate closely across the team to combine, modify, and implement core modeling components, including both conventional (e.g., attention, normalization, Mixture-of-Experts, FFNs) and unconventional components. You will ensure that they function optimally across our novel compute substrates.
• Performance Modeling & Scaling: Establish and test scaling laws specific to our novel hardware. Develop rigorous performance models to evaluate compute vs. memory trade-offs
• Advanced Mapping & Partitioning: Drive the partitioning and mapping of complex AI models down to hardware. Apply and invent advanced optimization strategies from first principles, including custom quantization schemes, sparsity/pruning, and distillation to fit the physical constraints of our substrates.
• GPU Optimization & Kernel Development: Develop and optimize GPU kernels using low-level programming models like CUDA, Triton, or CUTLASS. Profile and debug complex ML codebases to resolve performance bottlenecks (training and inference).
• Cross-Functional Collaboration: Act as a translator, discussing algorithmic trade-offs with theorists and converting model requirements into concrete specifications for infrastructure and hardware engineering teams.
Qualifications:
Required:
• An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math.
• Deep, practical understanding of the modern AI/ML stack and optimized compilation and execution of algorithms on modern GPU systems.
• Proven experience in profiling, identifying, and resolving performance bottlenecks in complex ML codebases.
• Demonstrated ability to map state-of-the-art AI model architectures (e.g., Transformers, Mixture of Experts, diffusion models) to system performance implications and apply advanced efficiency techniques such as sparsity, quantization, and distillation.
• Deep experience with PyTorch, including its internals, torch.compile, and distributed data parallel (DDP) / fully sharded data parallel (FSDP) libraries.
Preferred:
• A forward-looking perspective on co-designing algorithms for unconventional computing paradigms that map closely to the physics of underlying systems.
• Theoretical or research experience in advanced approximation/compression techniques beyond standard quantization.
Company:
Unconventional AI rethinks computer foundations to optimize energy efficiency for AI. Founded in 2025, the company is headquartered in San Francisco, USA, with a team of 11-50 employees. The company is currently Early Stage.