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Full-time
postgraduate degree
Health Benefits, 401k Matching, Unlimited PTO, Complimentary Meals
Posted 17d ago
~40 hrs/week
Remote in United States
Responsibilities
Develop the path from AI model architecture to physical silicon by creating training techniques and optimization strategies for novel compute substrates. You will focus on energy benchmarking, hardware-aware training, and low-level GPU kernel development to maximize efficiency.
Requirements
Requires an MS/PhD in a quantitative field with deep practical experience in the AI/ML stack and GPU performance profiling. Proficiency in PyTorch and the ability to map complex architectures like Transformers to system performance is essential.
Full job description
About Unconventional
Since 2022, AI has entered the mainstream, reshaping entire industries from education and software development to fundamental consumer behaviors. This revolution has created an unprecedented demand for computation - a demand that is now fundamentally limited by energy, not just in the datacenter, but at a global scale.
At Unconventional, our mission is to solve this. We are rethinking computing from the ground up to build a new foundation for AI that is 1000x more efficient. We're doing this by exploiting the rich physics of semiconductors, mapping neural networks directly to the device physics rather than relying on layers of inefficient abstraction.
The Role
As a Member of Technical Staff, AI Systems, Model Optimization, you will develop the path from model architecture to physical silicon. You will develop the training techniques, optimization strategies, and infrastructure required to make AI models run efficiently on our novel compute substrates, closing the loop between model design and tapeout.
What You'll Do
Energy Benchmarking & Performance Modeling: Develop rigorous performance models to evaluate compute, memory, and energy trade-offs. Track pareto-optimality across models and hardware configurations.
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.
Hardware-Aware Training: Develop and apply Quantization-Aware Training (QAT), noise-aware training, and sparsification techniques to adapt models to the physical constraints of our analog compute substrates, including memory footprint, connectivity, precision, and noise.
GPU Optimization & Kernel Development: Develop and optimize 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 between AI model architects and hardware/infrastructure engineering teams, converting model requirements into concrete specifications and codifying learnings for tapeouts.
Minimum Qualifications
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: 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.
Systems Fluency: 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.
Software Development: Deep experience with PyTorch, including its internals, torch.compile, and distributed data parallel (DDP) / fully sharded data parallel (FSDP) libraries.
Preferred Qualifications (Nice to Have)
Training Infrastructure: Experience with production-grade training frameworks (e.g., Megatron-LM, DeepSpeed) and distributed training at scale.
Unconventional Co-Design: A forward-looking perspective on co-designing training systems for unconventional computing paradigms that map closely to the physics of underlying systems.
Next-Gen Efficiency: Research or practical experience in advanced approximation/compression techniques beyond standard quantization, including noise-aware or physics-constrained training.
Why Join Us?
The Mission: Redefine computing for the next 50 years by solving the fundamental energy limitation of AI at a global scale.
The Impact: Shape the company's future as a foundational team member. Enjoy massive ownership and an outsized opportunity to drive change.
The Perks: A comprehensive package including best-in-class health benefits, 401k matching, truly unlimited PTO, and complimentary meals in our Palo Alto office.
Related keywords
AI SystemsModel OptimizationSemiconductorsNeural NetworksQuantizationSparsityDistillationCUDATritonCUTLASSPyTorchTorch.compileDDPFSDPMegatron-LMDeepSpeed
Building the most efficient substrate for intelligence.
Industry
Technology, Information and Internet
Company size
11-50 employees
Headquarters
San Diego, California
LinkedIn followers
11,772
Unconventional AI is rethinking the foundations of a computer to optimize energy efficiency for AI.
Founded by experts in AI systems, analog circuits, computing theory, and neuroscience, we are bringing biology-scale efficiency to artificial intelligence.
Offices: 6310 Meadowbrush Circle, San Diego, California 92130, US
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