RoCE Jobs

Databricks

Databricks is seeking a Senior Software Engineer for its AI Runtime (AIR) team. AIR is a managed platform designed for large-scale GPU training and fine-tuning of AI models, providing customers with on-demand access to accelerators and a serverless experience that simplifies the complexities of provisioning, scheduling, and orchestrating multi-node jobs. As a Senior Software Engineer on AIR, you will be instrumental in building and scaling the systems that ensure large-scale training is fast, reliable, and effortless. You will lead the architecture and evolution of the managed GPU training stack, focusing on areas such as scheduling, distributed training performance, fault tolerance, and the developer experience for large-scale job operations. Beyond hands-on system development, you will contribute to the technical vision for AIR, mentor engineers, and collaborate with product, research, and platform teams to expand the impact of custom AI training at Databricks.

$160k - $225k

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

Anduril Industries is seeking a Senior AI Infrastructure Engineer to lead the vision, execution, and long-term stability of how the company trains with GPUs at scale. This role involves taking ownership of cluster robustness, ensuring high-performance GPU systems are highly available, fault-tolerant, and resilient for ML platform and research teams. The primary focus is on logical stability and automated resilience, building self-healing mechanisms, tuning networking, and optimizing scheduling to ensure massive-scale training infrastructure runs seamlessly and scales efficiently.

$166k - $220k

Anduril Industries is seeking a Senior AI Infrastructure Engineer to lead the vision, execution, and long-term stability of how the company trains with GPUs at scale. This role involves taking ownership of cluster robustness, ensuring high-performance GPU systems are highly available, fault-tolerant, and resilient for ML platform and research teams. The primary focus is on logical stability and automated resilience, building self-healing mechanisms, tuning networking, and optimizing scheduling to ensure massive-scale training infrastructure runs seamlessly and scales efficiently.

$166k - $220k

Prime Intellect is building the open superintelligence stack, a full-stack system that unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment for post-training at frontier scale. We are seeking a Member of Technical Staff focused on GPU Infrastructure to join our team. This customer-facing role requires deep technical expertise to design, deploy, and optimize GPU cluster architectures for AI labs and ambitious AI teams. You will work at the intersection of frontier research, real infrastructure, and go-to-market for a new category of AI computing.

$150k - $300k

Baseten is seeking an engineer with strong machine learning experience and solid foundations in math and computer science to join our growing Post-Training team. This role is crucial for supporting the success of our customers' custom-trained models, which represent the majority of traffic on our platform. You will be responsible for building in-house tooling to efficiently train a wide spectrum of model architectures using various techniques at scale. This involves deep dives into technical topics and working across the full stack, from systems-level concepts like Kubernetes and storage to PyTorch distributed tensor computation and GPU kernels.

$200k - $275k

The Institute of Foundation Models (IFM) is seeking a deeply technical Senior Distributed Systems Engineer to co-design and optimize the communication stack for large-scale distributed training, including hybrid parallelism and Mixture-of-Experts (MoE) workloads. This systems-level engineering position focuses on performance engineering, distributed debugging, and communication-runtime co-design, aiming to improve performance, fault tolerance, and scalability across model architecture, communication systems, runtime, and hardware topology for ultra-scale GPU supercomputing systems.

$200k - $400k