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Ai Math Jobs in California (NOW HIRING)

Hyperbolic Labs

Head of Talent

San Francisco, CA · On-site

As we prepare for growth after our Series A, our team - led by co-founders with PhDs in AI, Math, and Computer Science - is poised to redefine computing. About the Role We're seeking a Head of Talent ...

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Ai Math information

What is the difference between Ai Math vs Data Analyst?

AspectAi MathData Analyst
Required CredentialsMathematics, Computer Science, AI certificationsStatistics, Data Analysis, Business Intelligence certifications
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness settings, consulting firms, corporate departments
Industry UsageAI development, machine learning projects, researchData 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:
Ai Math jobs near you
Infographic showing various Ai Math job openings in California as of June 2026, with employment types broken down into 73% Full Time, 21% Part Time, 1% Temporary, and 5% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution.

Hyperbolic Labs - Senior GPU Infrastructure Engineer

De Circle

San Francisco, CA • On-site, Remote

$127K - $173K/yr

Full-time

Posted 15 days ago

Job description

Hyperbolic Labs is on a mission to democratize AI by breaking down the barriers to computing power with our Open-Access AI Cloud. By aggregating computing resources across the globe, we offer an innovative GPU marketplace and AI inference service that promise affordability and accessibility for all. As pioneers at the intersection of AI and open-source technology, we believe in an open future where AI innovation is limited only by imagination, not by access to resources. We're looking for forward-thinking individuals who share our passion for making AI universally accessible, secure, and affordable. Join us in building a platform that empowers innovators everywhere to turn their visionary AI projects into reality.
As we prepare for growth after our Series A, our team - led by co-founders with PhDs in AI, Math, and Computer Science - is poised to redefine computing.
About the Role
We're seeking a Senior Infrastructure Engineer to help build and scale Hyperbolic's GPU Cloud Marketplace, by building a multi-tenancy provisioning and virtualization solution. This is a foundational role where you'll be responsible for transforming raw GPUs from diverse global suppliers into a programmable, orchestrated pool that serves thousands of AI developers and researchers. You'll work at the cutting edge of cloud infrastructure, building the core orchestration layer that enables our platform to deliver up to 75% cost savings compared to traditional cloud providers.
Who You Are
  • Deep understanding of bare-metal provisioning and lifecycle management, including IPMI/Redfish, BMC-based remote management, PXE boot, and automated OS deployment workflows
  • Deep understanding of GPU scheduling and orchestration, including GPU type awareness, memory management, topology considerations, placement strategies for multi-GPU jobs, and fragmentation minimization
  • Strong infrastructure and DevOps engineering skills with proficiency in Terraform or Pulumi, CI/CD for infrastructure, secrets management, configuration management, and observability stack implementation
  • Experience with storage and data infrastructure for AI/ML workloads, including object storage, high-IOPS block storage, and distributed file systems for training data and checkpoints
  • Proficiency with API design and cloud-init for automated provisioning and configuration
  • Solid understanding of GPU architecture, CUDA, and GPU compute optimization
  • Highly collaborative team player with excellent communication skills across technical and non-technical stakeholders
  • Proven ability to work effectively with hardware vendors and vendor engineering teams to troubleshoot issues and optimize integrations
  • Experience building and scaling cloud infrastructure or distributed systems in production environments

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