RDMA Jobs

Figure is an AI robotics company developing autonomous general-purpose humanoid robots with the goal of shipping human-level intelligence. The Helix team is seeking an experienced AI Training Performance Engineer to elevate model training. This role focuses on enhancing distributed training frameworks for large-scale model training, optimizing GPU kernels, evaluating different accelerator types, and co-designing models for maximum hardware utilization.

$200k - $400k

Together AI

We are seeking a software engineer with a strong focus on reliability to manage the production operations of our Kubernetes-native control plane, which provisions and runs our GPU inference fleet. This platform team develops the API and controllers that enable the inference team to define their needs, such as clusters, model deployments, or capacity changes, and ensures these are reconciled. Your role will be to maintain this functionality in the real world by troubleshooting issues like InfiniBand problems, diagnosing serving faults, and enhancing our automated self-healing systems to permanently resolve recurring failures. The primary goal is to ensure the decoupling remains effective even during disruptions and to maintain fleet efficiency without introducing new instabilities. You will be responsible for inference serving SLOs and error budgets, leading incident response, and implementing lessons learned from postmortems into system changes. Additionally, you will address the reliability aspects of capacity efficiency, recognizing that misconfigured defragmentation or bin-packing can pose production risks. We value candidates with hands-on production reliability experience in large-scale distributed or GPU-serving systems, proficiency in troubleshooting InfiniBand/RDMA and Kubernetes controller-based platforms, and a software engineering approach to GPU infrastructure.

$250k - $320k

Together AI

We are seeking a reliability-minded software engineer to manage the production operations of our Kubernetes-native control plane, which provisions and runs our GPU inference fleet. This platform team develops the manifest-driven API and controllers that enable the inference team to define their needs, such as cluster configurations, model deployments, and capacity adjustments, and ensures these are reconciled. Your role will be to maintain the reliability of this system in production, addressing issues like InfiniBand problems during node communication, diagnosing faults in hero-model and LoRA serving that generic health checks miss, and enhancing automated self-healing to permanently resolve recurring failure modes. The primary goal is to ensure the decoupling remains effective even during failures and to maintain fleet efficiency without introducing new instabilities. You will be responsible for inference serving SLOs and error budgets, leading incident response, and implementing systemic fixes based on postmortem analyses. Additionally, you will contribute to the reliability aspects of capacity efficiency, recognizing that misconfigured defragmentation or bin-packing can pose production risks. We value candidates with hands-on production reliability experience in large-scale distributed or GPU-serving systems, proficiency in troubleshooting InfiniBand/RDMA and Kubernetes controller/CRD-based platforms, and a software engineering approach to GPU infrastructure and its supporting tools.

$160k - $280k

SambaNova is seeking a Principal Engineer to own kernel-level drivers and low-level software for high-speed IO, memory, and networking subsystems within the DataScale® platform and RDU architecture. This role is central to delivering reliable, high-performance AI systems at scale. This is a hands-on Linux kernel and device driver role; it is not an application or platform software role. You will lead software bring-up of new silicon, deliver production drivers across the IO, memory, and network data paths, and debug complex issues spanning kernel, firmware, and hardware. This role suits an engineer with deep kernel and driver expertise and strong hardware fluency, who can operate in ambiguity and execute with high ownership.

$180k - $255k

Join NVIDIA’s Hyperscale Engineering team to develop networking solutions for large-scale AI infrastructure. You will work with customers and engineering teams to build, integrate, and improve software for BlueField and ConnectX platforms. Your work will span software development, system integration, troubleshooting, and performance analysis, helping customers move from initial testing to production deployment.

$184k - $357k

As a Senior AI Engineer, you will be at the forefront of our Generative AI initiatives, treating AI as a software engineering discipline. You will be responsible for the full lifecycle of our AI features, taking them from initial prototype to robust, scalable production services, while solving for real-world constraints like latency, error handling, and cost optimization. You’ll collaborate with a diverse range of clients to translate business needs into high-performance AI architectures. This role requires a blend of deep technical expertise in LLMs and a disciplined Software Engineering approach to ensure our solutions are robust, ethical, and scalable.

$159k - $219k

Cerebras Systems is seeking a senior front-end design engineer to join the world-class team designing and developing the next generations of the Cerebras Wafer Scale Engine (WSE). This role involves deep expertise in RTL design and integration, focusing on high-performance, power-efficient, and scalable solutions. You will collaborate closely with verification, physical design, software, and system teams to bring innovative semiconductor architectures from concept to production, tackling the unique challenges of building WSE systems. The company's architecture enables industry-leading AI training and inference speeds, transforming AI applications and unlocking real-time iteration and agentic computation.

$200k - $290k

Modal is building a new infrastructure layer for AI, focusing on the demands of AI workloads that previous infrastructure couldn't support. Our platform provides instant GPU access, sub-second container starts, and native storage, enabling customers to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We are experiencing rapid growth, with a significant increase in annualized run rate and a recent Series D funding round. Our team comprises experienced engineers and researchers from diverse backgrounds, including creators of popular open-source projects and academic researchers.

$220k - $300k