Posted 4 months ago
Research Engineer - AI Systems
@ Yotta Labs
Remote in Canada
Overview
Yotta Labs is hiring a full-time Research Engineer for AI Systems, fully remote and global. The role optimizes GPU and Trainium kernels, compilers and LLM inference runtimes across heterogeneous accelerators.
Responsibilities:
- Design high-performance kernels for Attention, MoE, GEMM and quantization
- Optimize kernels for NVIDIA, AMD and AWS Trainium
- Develop custom operators and graph optimizations
- Improve vLLM, SGLang and TensorRT-LLM inference runtimes
- Design distributed training and inference across thousands of accelerators
Requirements summary:
- Proficiency in Python and C++
- Deep GPU architecture and performance optimization knowledge
- Experience with CUDA, Triton, ROCm/HIP or AWS Neuron
- Familiarity with PyTorch, Dynamo and profiling tools
- Background in computer science or engineering preferred
Tools & skills
Source and dates
Employer career site / ATS feed · ashby
- Employer posting date
- 2026-06-28
- First seen by our source
- 2026-10-11
- Source record updated
- 2026-10-11
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Department | Software development |
| Hours per week | 40 (estimated) |
| Travel | air: none · ground: none |
| Work setting | Home |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Languages | English |
| Fields of study | Computer science, Engineering |
| Perks mentioned | Stock or equity |
Summary and details written by AI from the posting; the employer’s posting is the authority.
Yotta Labs
Saved company profile
Historical company information from a saved The Jobs API profile (LinkedIn/Crunchbase fields). It is not live-verified; figures may have changed. Missing values remain unknown.
Building the next generation multi-silicon AI cloud and runtime platform.
Full company description
Yotta Labs is at the forefront of building the next generation multi-silicon AI cloud and runtime platform. We enable training and inference across NVIDIA GPUs, AMD GPUs, and AWS Trainium/Inferentia, helping AI companies achieve the best performance and economics across heterogeneous hardware
- LinkedIn followers
- 741
- People associated on LinkedIn
- 7
- Reported office locations
- Seattle, US
- Business categories
- GPU
- Cloud Computing
- Artificial Intelligence (AI)
- Other recorded names
- Yotta Labs
- Profile identifies a recruitment agency
- No
- Reported total investment
- 0
Currency and observation date were not supplied; this is not a current valuation or a funding round.
Yotta Labs is at the forefront of building the next generation multi-silicon AI cloud and runtime platform. We enable training and inference across NVIDIA GPUs, AMD GPUs, and AWS Trainium/Inferentia, helping AI companies achieve the best performance and economics across heterogeneous hardware
| Field | Value |
|---|---|
| Industry | Technology, Information and Internet |
| Company size | 2-10 employees |
| Headquarters | Seattle |
| Organization type | Public Company |
| Year founded | 2024 |
| Open jobs on Jobsy | 3 |
| View page |
Location: Remote (Global)
Type: Full-time
Company: Yotta Labs
Apply: [email protected]
🧠 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 [email protected]. Please include links to any relevant projects or contributions.
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