---
title: 'AI Systems, Model Optimization at Unconventional, Inc.'
canonical: 'https://feeny.ai/job/ai-systems-model-optimization-unconventional-inc-mountain-view-ca-i-bhb7nvk4vwfb'
type: 'job'
last_seen: '2026-09-08'
---

# AI Systems, Model Optimization at Unconventional, Inc.

- **Company:** Unconventional, Inc.
- **Location:** Mountain View CA I, United States
- **Work type:** remote
- **Posted:** 2026-07-21
- **Last confirmed live:** 2026-09-08
- **Apply:** https://unconv.ai/careers/?gh_jid=4272105009

## 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.

## About Unconventional, Inc.

## Company Overview
- **One-liner**: Unconventional AI is rethinking the foundations of a computer to optimize energy efficiency for AI by building analog chips that run neural networks directly on physics rather than simulating digital logic.
- **Entity Type**: Private (Seed stage – raised $475M seed round)
- **Headquarters**: San Diego, California, United States (also has an office in San Francisco)
- **Founded**: Not explicitly stated; assumed ~2024/2025 based on funding announcement in December 2025
- **Founders**: Naveen Rao (CEO), Michael Carbin (co-founder), Sara Achour (co-founder), Meelan Lee (engineering leader)

## Core Business
- Primary industry/industries: Hardware design for artificial intelligence, analog computing, energy-efficient semiconductors
- Target customers: AI labs, hyperscalers, and enterprises that train and deploy large neural networks (B2B/Enterprise)
- Mission or purpose statement: *“Bringing biology-scale efficiency to artificial intelligence”* – building the right isomorphism for intelligence to unlock efficiency gains far beyond digital simulation

## Products & Services
- **Unconventional AI Chip Platform (in development)**: A novel analog/mixed-signal chip designed specifically for probabilistic AI workloads. The chip stores exact probability distributions in the physical substrate (silicon) rather than using numerical approximations, potentially achieving ~1,000x lower power consumption than digital computers. It is a **Hardware** product, supported by extreme codesign of algorithms and software.

## Market Standing
- **Valuation/Market Cap**: $4.5 billion (as of December 2025 – seed round valuation)
- **Key Metric**: Total Funding $475 million (seed round, December 2025)
- **Notable Investors/Partners**: Lightspeed Venture Partners (co-lead), Andreessen Horowitz (co-lead), Sequoia, Lux Capital, DCVC, Future Ventures, Jeff Bezos (individual), and others. CEO Naveen Rao personally invested $10 million.
- **Growth Signals**: 
  - Named to 2026 CB Insights AI 100 list
  - Headcount of 23 employees with +10.8% monthly growth
  - Monthly website traffic 27,433 (+21.7% growth)
  - Highly active on LinkedIn with 9,516 followers and frequent technical blog posts
  - Launched the “Unconventional Grant” program – $500,000 in funding for bold research ideas in AI compute

## Competitive Advantages
- **First-principles approach**: Rejects digital abstraction for analog/physics-based computation, achieving orders-of-magnitude energy efficiency improvements that conventional GPU architectures cannot match.
- **Extreme codesign**: Hardware, software, algorithms, and models are co-optimized from day one, not adapted from legacy computing paradigms.
- **World-class team**: CEO Naveen Rao (prior exits: Nervana→Intel, Mosaic→Databricks) plus co-founders with deep expertise in AI systems, analog circuits, computing theory, and neuroscience.
- **Massive financial backing**: $475M seed round at a $4.5B valuation provides a long runway for ambitious R&D.

## Strategic Focus
- **Near-term priorities**: Design and tape-out of analog AI chips; recruit top interdisciplinary talent across hardware, software, algorithms, and neuroscience.
- **Long-term direction**: Achieve “biology-scale” energy efficiency (on the order of 20W for human-level intelligence) and scale to meet projected global AI compute demand that could outstrip energy supply within 3–4 years.
- **Research investment**: Active grant program and open collaboration with academic/unconventional thinkers.

## Why Work Here
- **Culture**: Deeply interdisciplinary, first-principles culture that challenges assumptions – described as “endlessly curious, comfortable with discomfort, and drawn to the edge of chaos.”
- **Growth opportunity**: Very early-stage (only 23 employees) with massive ambition; employees can shape foundational technology from the ground up.
- **Remote/Hybrid**: Two offices (San Diego HQ and San Francisco); likely hybrid or on-site given hardware work, but not explicitly stated.
- **Perks & environment**: Small team, high ownership, access to top investors and mentors, and the chance to solve one of AI’s most critical bottlenecks (energy).
- **Talent sources**: Employees come from Databricks, MIT, Cohere, and other top AI/tech organizations – strong peer group.

## Sources
1. [unconv.ai](https://unconv.ai/)
2. [unconv.ai/careers](https://unconv.ai/careers/)
3. [unconv.ai/blog](https://unconv.ai/blog/introducing-unconventional-ai/)
4. [linkedin.com](https://www.linkedin.com/company/unconvai)
5. [a16z.com](https://a16z.com/announcement/investing-in-unconventional/)

## Other roles at Unconventional, Inc.

- [Member of Technical Staff - AI Training Platform](https://feeny.ai/job/member-of-technical-staff-ai-training-platform-unconventional-inc-mountain-view-d1g7x3es47d9) — Mountain View, CA / Seattle, WA
- [Finance Manager, Accounting & FP&A](https://feeny.ai/job/finance-manager-accounting-fp-a-unconventional-inc-mountain-view-s7qznaybwxbd) — Mountain View, CA
- [Member of Technical Staff - Development Experience](https://feeny.ai/job/member-of-technical-staff-development-experience-unconventional-inc-mountain-sb1ydxb6g5rk) — Mountain View, CA / Seattle, WA
- [Member of Technical Staff - Agentic Applications](https://feeny.ai/job/member-of-technical-staff-agentic-applications-unconventional-inc-mountain-view-d5s7z32mqy23) — Mountain View, CA / Seattle, WA
- [AI Theory & Systems, Algorithms & Models](https://feeny.ai/job/ai-theory-systems-algorithms-models-unconventional-inc-mountain-view-f0vwf70pgvkv) — Mountain View, CA
- [AI Silicon, Silicon Validation Engineer](https://feeny.ai/job/ai-silicon-silicon-validation-engineer-unconventional-inc-mountain-view-bnzvp2npndhw) — Mountain View, CA
- [AI Silicon, Junior Digital Design Engineer](https://feeny.ai/job/ai-silicon-junior-digital-design-engineer-unconventional-inc-mountain-view-sz9k072xz7hm) — Mountain View, CA
- [AI Silicon, Digital Design Engineer](https://feeny.ai/job/ai-silicon-digital-design-engineer-unconventional-inc-mountain-view-7nbkzmsh4caz) — Mountain View, CA
- [AI Systems, Language & Reasoning Models](https://feeny.ai/job/ai-systems-language-reasoning-models-unconventional-inc-mountain-view-ca-i-ak7pqwr2gn32) — Mountain View CA I, United States
- [AI Silicon, Design Verification](https://feeny.ai/job/ai-silicon-design-verification-unconventional-inc-mountain-view-93xaam2wwy1b) — Mountain View, CA