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Home Based Algorithmic Trading Programmer Jobs in California

AI Systems

Palo Alto, CA · On-site

$123K - $168K/yr

Act as a translator, discussing algorithmic trade-offs with theorists and converting model ... engineering teams. Qualifications : Required : • An MS/PhD or equivalent research/project ...

Hudson River Trading (HRT) is a quantitative trading firm at the forefront of technological ... Strong understanding of data structures, algorithms, and design principles. * Excellent ...

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189K/yr

Design, develop, and optimize GPU-based algorithms and data structures to accelerate ZKP proof systems and related computations. * Collaborate with the engineering team to identify performance ...

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189K/yr

Design, develop, and optimize GPU-based algorithms and data structures to accelerate ZKP proof systems and related computations. * Collaborate with the engineering team to identify performance ...

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Home Based Algorithmic Trading Programmer information

What cities in California are hiring for Home Based Algorithmic Trading Programmer jobs? Cities in California with the most Home Based Algorithmic Trading Programmer job openings:
Infographic showing various Home Based Algorithmic Trading Programmer job openings in California as of July 2026, with employment types broken down into 26% Locum Tenens, 1% As Needed, 63% Full Time, 8% Part Time, 1% Temporary, and 1% Contract. Highlights an 84% Physical, 1% Hybrid, and 15% Remote job distribution.

$123K - $168K/yr

Full-time

Posted 14 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.