Member of Technical Staff – GPU Performance Engineer

🕒 March 27

🌐 United States, United Kingdom, +1 more countries – Remote

🇺🇸 United States – Remote 🇬🇧 United Kingdom – Remote 🇸🇬 Singapore – Remote

⏰ Full Time

🔴 Lead

🖥 Software Engineer

👻 Ghost score 69%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Reka AI

WebsiteLinkedInAll Job Openings

1 - 10 employees

Founded 2022

🤖 Artificial Intelligence

🏢 Enterprise

☁️ SaaS

💰 $58M Series A on 2023-06

Artificial Intelligence • Enterprise • SaaS

Reka AI is a company specializing in the development of cutting-edge multimodal AI models. These models are designed to empower AI agents with the ability to see, hear, and speak, facilitating a range of applications from text and code to images, video, and audio data. Reka's proprietary models, including Reka Core, Flash, Edge, and Spark, are created from scratch with innovative architectures that allow for deployment on devices, on premises, or in the cloud. Known for advancing the field of AI research, Reka AI collaborates with global technology partners to expand access to its advanced multimodal AI capabilities.

📋 Description

• Design and implement improvements to training infrastructure • Directly contribute to technical decisions optimizing performance of models • Work on post-training processes, including reinforcement learning and fine-tuning • Improve the efficiency and scalability of model serving infrastructure

🎯 Requirements

• Strong engineering skills with fluency in Python and PyTorch (or other frameworks) • Proven experience implementing and training large deep learning models • Experience writing and debugging low-level GPU code (CUDA, C++) • Experience scaling up GPU jobs using large-scale compute clusters (e.g., Slurm or Kubernetes) • Demonstrated ability to analyze and optimize the performance of GPU-accelerated workloads, including profiling, identifying bottlenecks, and implementing performance tuning techniques

🏖️ Benefits

• Five weeks of paid leave to recharge • Comprehensive healthcare benefits (including vision and dental) • Additional perks that support your well-being • Visa assistance, including H1B and OPT transfers, for US employees