--- title: 'Research Engineer - AI Systems at Yotta Labs' canonical: 'https://feeny.ai/job/research-engineer-ai-systems-yotta-labs-united-states-rw7ey5zxc2ps' type: 'job' last_seen: '2026-09-09' --- # Research Engineer - AI Systems at Yotta Labs - **Company:** Yotta Labs - **Location:** United States - **Employment:** full-time - **Work type:** remote - **Posted:** 2026-06-28 - **Last confirmed live:** 2026-09-09 - **Apply:** https://jobs.ashbyhq.com/yotta/5ad886ff-a109-424e-910d-bb764a5201e9 ## Job description Location: Remote (Global) Type: Full-time Company: Yotta Labs Apply: careers@yottalabs.ai 🧠 About Yotta Labs Yotta Labs is building the next generation multi-silicon AI cloud and runtime platform to power the world’s most demanding AI workloads. We enable training and inference across NVIDIA GPUs, AMD GPUs, and AWS Trainium, helping AI companies achieve the best performance and economics across heterogeneous hardware. Our mission is to provide high-performance AI computing and Model API services, enabling AI companies, research labs, and enterprises to train, deploy and integrate cutting-edge models at scale. 🛠️ Role Overview We are seeking a highly motivated AI Systems Research Engineer specializing in Trainium, GPU kernels, and LLM systems optimization. You will work at the intersection of AI Systems, Compiler and Runtime Optimization, Distributed Training & Inference, GPU/Accelerator Kernel Development, and Large Language Model Infrastructure. Your work will directly impact the scalability and performance of AI applications deployed on our platform. 🎯 Responsibilities - Design and implement high-performance kernels for Attention, MoE, GEMM, collective communication, and quantization. - Optimize kernels for NVIDIA, AMD, and AWS Trainium. - Develop custom operators and graph optimizations using Neuron SDK, PyTorch/XLA, Torch Dynamo, and Neuron Compiler. - Improve performance of vLLM, SGLang, TensorRT-LLM, and custom inference runtimes. - Design scalable distributed training and inference solutions across thousands of accelerators. - Contribute to open-source projects, publish technical findings and engage with the developer community. ✅ Qualifications - Proficiency in AI programming languages such as Python and C++. - Deep understanding of GPU architecture and performance optimization. - Experience with CUDA, Triton, ROCm/HIP, or AWS Neuron. - Strong understanding of AI frameworks (e.g., PyTorch, Dynamo, LMCache), model architectures and profiling tools (e.g. Nsight, ROCm Profiler, or Neuron Profiler). - Strong problem-solving skills and the ability to work in a collaborative, remote environment. - A background in computer science, engineering, or a related field is preferred. 🌟 Preferred Experience - Contributions to open-source AI infra projects like vLLM, SGLang, PyTorch, or Triton. - Experience with with FlashAttention, PagedAttention, MoE, RLHF, or distributed AI systems. - Publications in top-tier conferences like MLSys, OSDI, SOSP, NSDI, SC, HPCA, or ISCA 🌐 Why Join Yotta Labs? - Be part of a visionary team aiming to redefine AI infrastructure and influence the future of multi-silicon AI computing. - Work on cutting-edge technologies that solves frontier AI infrastructure problems. - Collaborate with experts from leading institutions and tech companies. - Competitive compensation with equity. Enjoy a flexible, remote work environment that values innovation and autonomy. 📩 How to Apply Interested candidates should apply directly or send their resume and a brief cover letter to careers@yottalabs.ai. Please include links to any relevant projects or contributions. ## About Yotta Labs ## Company Overview - **One-liner**: Yotta Labs is building an interoperable AI infrastructure operating system that orchestrates AI workloads across multi-cloud and multi-silicon environments, turning fragmented GPU capacity into a unified execution fabric. - **Entity Type**: Private (Seed Stage) - **Headquarters**: Seattle, Washington, USA - **Founded**: 2024 - **Founders**: Da Li (CEO) and a team of experts in AI and High-Performance Computing (HPC) ## Core Business - **Primary Industry**: AI Infrastructure / Cloud Computing / Decentralized Compute - **Target Customers**: AI-native and enterprise teams deploying production AI systems (training, fine-tuning, and inference) - **Mission/Purpose**: To make AI compute interoperable, elastic, and efficient by default, enabling workloads to move fluidly across clouds, regions, and silicon generations without vendor lock-in. ## Products & Services - **Interoperable AI OS (Yotta Platform)**: A unified execution and orchestration control plane that abstracts differences across cloud providers and GPU architectures (NVIDIA, AMD, emerging accelerators). Enables multi-cloud, multi-silicon AI workload deployment and scheduling from a single pane of glass. - **Compute Products (under the Yotta OS)**: - **Pods**: On-demand GPU environments (VMs) for training and inference. - **Serverless**: Automatically scaling inference and batch processing across regions. - **Launch Specs**: Pre-configured, one-click deployment specs for instant setup. - **Open Source Tools**: High-performance GPU kernels for AMD inference (GitHub) and optimized neural network memory management. - **Decentralized OS (DeOS)**: A protocol and network for orchestrating workloads across geo-distributed GPUs globally, aiming for "Yottascale" (1 million times exascale) processing. ## Market Standing - **Valuation/Market Cap**: Not publicly disclosed. - **Key Metrics**: - **Total Funding**: $300,000 in Grant funding; a Seed Round (led by Big Brain Holdings, Eden Block) raised on October 30, 2025 (with 6 investors, amount undisclosed). - **Scale**: Reports over 1 million Pods deployed and 50,000+ developers on platform. Operates in 20+ global regions. - **Notable Investors/Partners**: Big Brain Holdings, Eden Block; National Science Foundation (NSF) grant recipient; Advisory board includes Jack Dongarra (ACM A.M. Turing Award winner). - **Growth Signals**: Headcount has grown +166.7% (from 2 to ~6 employees) since founding in 2024. Active job postings have grown +1500% yearly, with 16 open roles. Recent high-profile media coverage in VentureBeat ("Interoperable AI OS for multi-cloud compute liquidity"). ## Competitive Advantages - **Multi-Silicon & Multi-Cloud Abstraction**: Unlike single-cloud solutions, Yotta is architected to treat heterogeneous infrastructure (NVIDIA, AMD, and emerging accelerators) as a first-class feature, not a workaround. - **Hardware-Aware Scheduling**: Dynamically routes workloads to the most cost-effective and available hardware across different regions and power grids, enabling lower costs and higher utilization. - **Production-First Design**: Built for reliability, observability, and enterprise compliance (SOC 2), making it suitable for production AI teams rather than experimental demos. - **Elastic & Interoperable Compute**: The ability to move workloads across clouds and regions without rewriting infrastructure logic solves the "lock-in" problem for large-scale AI. ## Strategic Focus - **Building the Interoperable AI OS**: The current priority is establishing Yotta as the default operating system for multi-cloud, multi-silicon AI execution. - **Decentralized Orchestration**: Expanding the DeOS protocol to unlock compute liquidity from smaller, stranded, or underutilized GPU resources globally. - **Scaling the Team**: Aggressively hiring across Engineering (GPU Cloud Platform, Research Engineers), Developer Relations (AI Developer Advocates), and GTM roles to build out the product and user base. ## Why Work Here - **Culture & Mission**: Working on a foundational problem in AI infrastructure (fragmentation and vendor lock-in) with a high-impact, research-driven team. - **Team & Advisory**: Backed by a team with deep expertise in distributed systems, HPC, and national labs, advised by Turing Award winner Jack Dongarra. - **Work Location**: Remote-friendly with an HQ in Seattle, Washington, USA. Several open roles list "Remote" as an option. - **Growth Stage**: Early-stage startup (~6 employees) with massive headcount growth plans (+1500% YoY in job postings), offering significant ownership and impact for early hires. - **Technical Depth**: Work involves kernel optimization, distributed execution, scheduling, and reliability at scale across cutting-edge hardware (H100, B200, AMD GPUs). ## Sources 1. [yottalabs.ai](https://www.yottalabs.ai/) 2. [LinkedIn](https://www.linkedin.com/company/yotta-labs) 3. [VentureBeat](https://venturebeat.com/business/interoperable-ai-os-for-multi-cloud-compute-liquidity-inside-yotta-labs) 4. [docs.yottalabs.ai](https://docs.yottalabs.ai/) 5. [Built In](https://builtin.com/company/yotta-labs) ## Other roles at Yotta Labs - [Research Engineer Intern - AI Systems](https://feeny.ai/job/research-engineer-intern-ai-systems-yotta-labs-united-states-277v5vq84m8f) — United States - [Sales Manager – GPU Compute & AI Model APIs](https://feeny.ai/job/sales-manager-gpu-compute-ai-model-apis-yotta-labs-united-states-1e6jc75ddqea) — United States - [GPU Cloud Platform Engineer](https://feeny.ai/job/gpu-cloud-platform-engineer-yotta-labs-united-states-fevfkq8ppjmw) — United States