About Ayo
We are a venture-backed early-stage startup developing processors specialized for machine learning. The processor will provide orders or magnitude improvement in speed and power efficiency with a goal of unseating the GPU as the dominant computing platform for AI.
The Role
We are seeking a deep AI practitioner to join as a founding team member - someone with hands-on experience working on or alongside a foundation model at scale, who understands what happens under the hood when splitting jobs across thousands of GPUs, and who is excited to bring that depth to novel hardware. They have a full-stack understanding of machine learning architectures, love to optimize algorithms across disciplinary boundaries, and will deploy and train models directly on our prototype chips to help us prove out what our processor can do - no prior hardware experience required.
What You’ll Do:
Deploy and run trained models on prototype hardware and digital twins, producing working demonstrations on our chips.
Develop and adapt algorithms to train models on novel processing environments, including our prototype hardware.
Work with hardware engineers to define and refine processor architecture based on insights learned through model training and experimentation.
Maintain a deep curiosity about what makes AI systems work - and bring that curiosity to bear on how they run on new hardware.
Support go-to-market strategy development
What We’re Looking For:
PhD in machine learning, representation learning, theory of computation, or a related field - or equivalent industry experience working on foundation models at scale.
Experience training models at scale - distributed training across many GPUs, working with large datasets and compute.
Has built and trained neural networks from scratch
Deep knowledge of the structure and internal operation of neural networks - including how and why they behave the way they do (e.g. interpretability or explainability work is a plus).
Excitement about applying deep AI expertise to new and novel hardware environments - you don’t need prior experience with photonics or silicon, but you want to learn.
Fluent knowledge of Python
Fluency in PyTorch (preferred), TensorFlow, JAX, or other industry-standard ML software libraries
Why Ayo
You'll be a founding AI team member - shaping how we think about and deploy AI as a company.
We are working on a genuinely hard and interesting problem: unseating the GPU as the dominant AI compute platform.
Early-stage means real ownership, real impact, and meaningful equity.
Competitive salary. Equity commensurate with stage and seniority. Benefits package including health, dental, and vision.
Fully onsite in Boston - we are a collaborative, in-person team.
Skills Required
- PhD in machine learning or related field
- Experience training models at scale across many GPUs
- Has built and trained neural networks from scratch
- Deep knowledge of neural networks
- Fluent knowledge of Python
- Fluency in PyTorch, TensorFlow, or JAX
Ayo Seminconductor Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Ayo Seminconductor and has not been reviewed or approved by Ayo Seminconductor.
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Equity Value & Accessibility — Job postings describe competitive salary plus equity aligned with stage and seniority, and highlight meaningful ownership and impact for early team members. Early‑hire framing (being among the first employees) indicates tangible equity participation alongside cash compensation.
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Healthcare Strength — Some materials reference health, dental, and vision coverage being included, indicating core medical benefits are available. Although specifics are not published, this points to baseline healthcare support in the package.
Ayo Seminconductor Insights
What We Do
Ayo is a venture-backed early-stage startup that will radically re-define how humanity performs compute for AI. Our photonic processors will provide orders of magnitude improvement in speed and power efficiency in comparison to conventional digital compute platforms by taking advantage of the natural parallelism, speed and energy efficiency of light.
