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Ai Math Training Jobs in California (NOW HIRING)

Unconventional AI

AI Systems, Training

Palo Alto, CA · On-site

$123K - $168K/yr

... training platform and co-design training systems alongside novel AI models and hardware ... Math. • Experience: Veteran of the modern ML software stack. Demonstrated ability to map ...

Sciforium

LLM Training Engineer

San Francisco, CA · On-site

$155K - $220K/yr

About the Role As an LLM Training Engineer , you'll work across the full foundation-model stack ... MS or PhD in Computer Science, Machine Learning, AI, Mathematics, or related field Benefits include

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Ai Math Training information

What are some common challenges faced in an AI Math Training role?

One of the main challenges in an AI Math Training position is translating complex mathematical theories into practical algorithms that can be efficiently implemented in machine learning models. You may also encounter difficulties when training models on large datasets, balancing computational resources with accuracy, and ensuring models generalize well to unseen data. Collaboration with data scientists, engineers, and domain experts is frequent, requiring strong teamwork and communication skills. Staying current with rapidly evolving AI and mathematical techniques is essential for success and ongoing career development in this field.

What are the key skills and qualifications needed to thrive in the Ai Math Training position, and why are they important?

To thrive in an AI Math Training role, you need a strong background in mathematics, statistics, and machine learning, often supported by a degree in mathematics, computer science, or a related field. Experience with programming languages (such as Python or R), deep learning frameworks (like TensorFlow or PyTorch), and relevant AI or data science certifications are typically required. Strong analytical thinking, clear communication, and problem-solving abilities help in conveying complex concepts and collaborating with diverse teams. These skills are essential for effectively developing and training AI models that rely on advanced mathematical principles and for communicating technical findings to both technical and non-technical stakeholders.

What is an AI Math Training job?

An AI Math Training job involves developing, refining, and optimizing mathematical models used in artificial intelligence systems. This role often includes curating datasets, training AI algorithms, and ensuring mathematical accuracy in machine learning models. Professionals in this field typically have expertise in linear algebra, calculus, statistics, and optimization techniques. They work closely with data scientists and engineers to improve AI efficiency and reliability.

What are the most commonly searched types of Ai Math Training jobs in California? The most popular types of Ai Math Training jobs in California are:
What are popular job titles related to Ai Math Training jobs in California? For Ai Math Training jobs in California, the most frequently searched job titles are:
What job categories do people searching Ai Math Training jobs in California look for? The top searched job categories for Ai Math Training jobs in California are:
What cities in California are hiring for Ai Math Training jobs? Cities in California with the most Ai Math Training job openings:
Ai Math Training jobs near you
Infographic showing various Ai Math Training job openings in California as of July 2026, with employment types broken down into 76% Full Time, 20% Part Time, 1% Temporary, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution.

AI Systems, Training

Unconventional AI

Palo Alto, CA • On-site

$123K - $168K/yr

Full-time

Re-posted 8 days ago

Job description

Job Summary:
Unconventional AI is a company focused on redefining computing to solve the energy limitations of AI. They are seeking a key contributor to build a next-generation ML model training platform and co-design training systems alongside novel AI models and hardware.
Responsibilities:
• Build and maintain highly optimized, model-specific training stacks specifically tuned for state-of-the-art generative vision, language, and world models.
• Design and scale multi-node distributed training systems, implementing elastic sharding and robust data streaming pipelines for fast, large-scale iteration. Implement and robust model checkpointing and recovery mechanisms.
• Develop and optimize kernels using low-level programming models like CUDA and Triton. Design rigorous benchmarking suites to track Model Flops Utilization (MFU), memory bandwidth, and convergence stability.
• 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:
• Education: 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 the modern ML software stack. Demonstrated ability to map state-of-the-art AI model architectures (e.g., transformers, Mixture of Experts, diffusion models) to system performance implication. Deep expertise in how models are partitioned across a cluster, with a mastery of communication primitives, and parallelism strategies.
• Software Development: Proven track record of implementing, debugging, and maintaining production-grade training frameworks—such as Megatron-LM, DeepSpeed, Ray, PyTorch Lightning—turning raw compute into a reliable model-building factory.
Preferred:
• Unconventional Co-Design: A forward-looking perspective on co-designing algorithms for unconventional computing paradigms that map closely to the physics of underlying systems.
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.

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