--- title: 'Research Engineer Intern - AI Systems at Yotta Labs' canonical: 'https://feeny.ai/job/research-engineer-intern-ai-systems-yotta-labs-united-states-277v5vq84m8f' type: 'job' last_seen: '2026-09-09' --- # Research Engineer Intern - AI Systems at Yotta Labs - **Company:** Yotta Labs - **Location:** United States - **Employment:** internship - **Work type:** remote - **Posted:** 2026-08-02 - **Last confirmed live:** 2026-09-09 - **Apply:** https://jobs.ashbyhq.com/yotta/09821a51-fbe6-42a7-a566-0d2b5d40fae3 ## Job description Location: Remote (Global) Type: Internship 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 Research Engineer Intern to work on Trainium, GPU kernels, and LLM systems optimization. Over a 12–16 week internship, you will own a well-scoped project at the intersection of AI Systems, Compiler and Runtime Optimization, Distributed Training & Inference, GPU/Accelerator Kernel Development, and Large Language Model Infrastructure — taking it from design to working, profiled code running on real hardware. Your work will ship to production or open source and directly impact the performance of AI applications deployed on our platform. Strong interns receive return offers for full-time roles. 🎯 Responsibilities - Implement and optimize compute kernels for Attention, GEMM, MoE, and quantization on NVIDIA, AMD, or AWS Trainium. - Build custom operators using CUDA, Triton, ROCm/HIP, or the Neuron SDK with PyTorch/XLA. - Profile and improve inference performance in vLLM, SGLang, and our custom runtimes — kernel fusion, scheduling, KV-cache and memory optimizations. - Build benchmarks, chase down performance regressions, and turn profiler traces into concrete speedups. - Ship code upstream to open-source AI infrastructure projects, with tests and documentation. ✅ Qualifications - Currently pursuing a BS, MS, or PhD in Computer Science, Computer Engineering, or a related field. - Solid programming skills in Python and familiarity with C++. - Understanding of GPU/accelerator architecture fundamentals (memory hierarchy, parallelism, occupancy) from coursework, research, or projects. - Experience writing CUDA, Triton, ROCm/HIP, or Neuron kernels — class projects and personal projects count. - 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 independently in a collaborative, remote environment. 🌟 Preferred Experience - Contributions to open-source AI infra projects like vLLM, SGLang, PyTorch, or Triton. - Familiarity with LLM inference internals — FlashAttention, PagedAttention, continuous batching, speculative decoding, MoE, or quantization. - Experience with profiling tools (e.g. Nsight, ROCm Profiler, Neuron Profiler, or PyTorch Profiler) and performance debugging on real workloads. - 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 frontier AI infrastructure problems with access to serious hardware — latest-generation NVIDIA GPUs, AMD accelerators, and AWS Trainium at scale. - Get direct mentorship from engineers from leading institutions and tech companies. - Competitive internship compensation, a flexible remote work environment, and a fast path to a full-time return offer for top performers. 📩 How to Apply Interested candidates should apply directly or send their resume to careers@yottalabs.ai. Please include links to any relevant projects or contributions (GitHub, open-source PRs, course projects) — for internships, these matter more to us than a cover letter. ## 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 - AI Systems](https://feeny.ai/job/research-engineer-ai-systems-yotta-labs-united-states-rw7ey5zxc2ps) — 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