GPU Jobs

Sierra is seeking a Software Engineer for its Inference team to build the systems that power their AI agents. This role focuses on making foundation models fast, reliable, and efficient at scale. You will define the inference architecture, working on systems for serving, routing, capacity management, and optimization for latency, reliability, and cost. This is a systems-first role at the intersection of distributed infrastructure and AI, requiring strong distributed systems fundamentals and experience with complex systems problems.

$230k - $390k

Baseten powers mission-critical inference for leading AI companies, enabling them to bring cutting-edge models into production. We are seeking distributed systems engineers and product-minded generalists to build the distributed runtime that powers large-scale LLM inference on Baseten. Our inference platform ensures customers can deploy and operate advanced models with industry-leading performance, scalability, and reliability. You will work across the entire stack, from the developer experience for model deployment to the systems orchestrating Kubernetes and routing traffic efficiently, ensuring every model on our platform is fast, reliable, and cost-efficient.

$180k - $360k

Baseten powers mission-critical inference for leading AI companies, enabling them to bring cutting-edge models into production. We are seeking inference performance engineers to make demanding AI workloads run faster and more efficiently. You will work across the entire stack, from the inference engine and runtime through scheduling, serving, and routing, applying advanced techniques to optimize performance. This role is ideal for someone who thrives in a fast-paced startup environment and is eager to make significant contributions to LLM inference.

$180k - $360k

Runpod is seeking a systems-focused Cloud Infrastructure Security Engineer to safeguard its bare-metal and virtualized GPU cloud infrastructure. This role is crucial for ensuring the security, multi-tenant isolation, and integrity of the underlying GPU cloud infrastructure. The ideal candidate will have deep knowledge of Linux systems, kernel internals, hypervisors, and containerization, focusing on preventing tenant breakouts, securing GPU hardware, and building resilient infrastructure for AI and machine learning workloads. You will operate with an attacker's mindset, identifying and patching vulnerabilities at the lowest levels of the stack.

$152k - $175k

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

NVIDIA's Math Libraries team is seeking a senior engineer to contribute to kernel generation for AI and HPC, focusing on matrix operations, JITing, and fusions. This role involves developing GPU-accelerated mathematical libraries that power advancements in AI, scientific simulations, and data analytics across various industries. You will be instrumental in enabling leading organizations to revolutionize fields like healthcare, NLP, VR, and autonomous vehicles.

$184k - $357k

We are seeking a Senior Software Engineer to join our team focused on enabling CUDA Math Libraries on new and specialized platforms. This role is crucial for ensuring that NVIDIA's math libraries deliver high functionality and performance across a wide range of targets, supporting the AI, data analytics, and scientific simulation industries. You will be responsible for the integration, functional qualification, compliance, and performance readiness of these libraries on emerging platforms, collaborating closely with core library and devops engineering teams. This is an exciting opportunity to contribute to the forefront of application performance in the AI era.

$152k - $288k

At Mach9, ML infrastructure engineers build and maintain the systems that power production AI models for civil engineering and surveying. Our ML pipeline spans 10,000+ miles of labeled survey data, image segmentation networks, and 3D prediction models serving real-time inference to surveyors and engineers in the field. This role is ideal for mid-career ML infrastructure engineers with experience building for both training and inference. You'll build training pipelines that handle deep transformer models on hundreds of terabytes of 3D point cloud and image data. You'll also architect our inference infrastructure, delivering both heavy offline detection algorithms and real-time responsive inference that integrates directly with our CAD software.

$160k - $250k

We are seeking a Software Engineer, Inference to be responsible for the reliability, scale, and efficiency of the systems that serve our AI models to users. This production-facing systems role is central to the company, acting as a bridge between cutting-edge inference techniques and the practicalities of serving live traffic, including rollouts, capacity management, and incident response. You will ensure that performance gains from our research and inference teams reach production safely and remain operational, powering our live serving platform and related products.

$300k - $400k

NVIDIA is seeking an AI Compute Engineer to join its Infrastructure Specialists team. This role involves working on some of the world's largest and fastest AI Compute systems, supporting academic, commercial, and government groups. The engineer will interact with customers, partners, and internal teams to analyze, define, and implement large-scale AI Compute projects, encompassing networking, system design, automation, and validation.

$124k - $196k

Runpod is seeking a Senior ML Systems Engineer focused on Inference to lead efforts in making LLM inference the fastest and most cost-efficient on their platform. This role involves owning LLM serving performance end-to-end, measuring, understanding, and improving it across various models, hardware, and workloads. The work directly impacts customer latency and cost, requiring a hands-on approach to identify and fix bottlenecks, and ensure reliable production deployment. The ideal candidate will be passionate about AI infrastructure and contribute to building the platform for the next generation of AI developers.

$150k - $220k

NVIDIA is seeking a Senior AI Compute Engineer to join its Infrastructure Specialists team. This role involves working with academic, commercial, and government groups globally to revolutionize deep learning and data analytics, and to power data centers. You will be part of a team building some of the world's largest and fastest AI Compute systems. The position requires strong interpersonal skills to interact with customers, partners, and internal teams to analyze, define, and implement large-scale AI Compute projects, encompassing networking, system design, automation, and validation.

$148k - $288k

Datadog is seeking a customer-focused, deeply technical Distinguished Architect to join our Product Solutions Architecture team. In this role, you will act as a technical multiplier for leading AI labs and AI-native companies, bridging the gap between their infrastructure aspirations and Datadog’s technology roadmap. You will ensure our platform natively solves the unique observability challenges of training and deploying foundational models at scale. We operate as a hybrid workplace to foster collaboration and creativity while ensuring work-life harmony.

$330k - $440k

NVIDIA is seeking a Senior Software Engineer to lead the bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at the largest scales. In this role, you will set technical direction for communication libraries, model frameworks, and inference/training stacks to ensure state-of-the-art LLM workloads run efficiently and reliably at scale. You will lead deep performance and reliability investigations on multi-GPU and multi-node deployments, define how new platforms are benchmarked and qualified, and build the resilience and failure-attribution capabilities essential for large clusters. This is a hands-on, senior individual-contributor role for an engineer experienced at the intersection of deep learning systems, GPU performance, distributed computing, and large-scale operations.

$184k - $357k

Joining NVIDIA's DGX Cloud AI Efficiency Team means advancing the performance, efficiency, and resiliency of large-scale AI workloads. You will help AI researchers and platform teams understand end-to-end behavior across GPUs, networking, storage, and software stacks. Your work will shape scalable DGX Cloud systems, turn complex measurements into prioritized engineering decisions, and continuously raise the performance and reliability of AI workloads. Join a technically diverse team of infrastructure experts to unlock more efficient AI at scale.

$224k - $431k

NVIDIA is seeking a dedicated and creative Security Engineer to join our GPU System Software team. In this role, you will be instrumental in developing mitigations, conducting security research, and building advanced security platforms and frameworks to identify vulnerabilities in complex systems software. This is an exceptional opportunity to contribute to the security of foundational technologies in gaming, professional graphics, and cloud computing, as well as AI, data centers, and healthcare. You will be part of an inclusive team focused on maintaining an industry-leading security posture for groundbreaking products.

$152k - $288k

The Core ML team is seeking an engineer to map novel algorithms for efficient large-scale AI training and inference to the Cerebras architecture. This role involves determining when these algorithms outperform competing approaches and how their advantages scale with models, workloads, and hardware. You will combine analytical performance modeling, empirical benchmarking, and hands-on prototyping to characterize the efficiency frontiers of emerging ML algorithms. Your work will span kernel-level and end-to-end performance, influencing research directions, implementation on current systems, and future hardware/software capabilities.

Chai Discovery is building the computer-aided design suite for molecules, with major pharmaceutical companies already using their platform to accelerate drug discovery. They are at the forefront of reinventing life sciences through AI, developing frontier models that understand biochemical structure and interaction. This role is an opportunity to push the boundaries of AI drug design alongside a skilled and dedicated team.

Together AI

Together AI is building the AI Native Cloud, an end-to-end platform for the full generative AI lifecycle, combining a fast LLM inference engine with state-of-the-art GPU cloud infrastructure. The Together Cloud team builds the flagship IaaS product that provides high-performance, AI-ready GPU clusters through a self-serve cloud console, along with the virtualized infrastructure layer powering Together's inference, RL, and fine-tuning products. As a Senior Software Engineer focusing on AI Compute, you will build and own major components of the next generation AI cloud platform – a highly available, global cloud infrastructure with cutting-edge virtualization of the latest ML hardware. This virtualized computing platform powers our own SaaS products and serves external cloud customers through self-serve offerings. The hard problems in rapidly scaling heterogeneous GPU fleets are software problems that we solve by building global and in-DC services, Kubernetes operators, and high-performance SDN libraries, forking hypervisors and Linux kernels, and building infra tailored to inference and fine-tuning. You'll own massive greenfield projects across their full lifecycle – scoping the problem, designing the system, and building it through to GA launch.

$220k - $270k

As a Senior Engineering Manager, you will lead an engineering team focused on making large language models run faster, cheaper, and more reliably in production. This role balances team leadership with core systems and performance engineering on demanding models in production, requiring you to stay deeply technical and hands-on with the inference stack end to end. The work is applied, not academic, with optimizations landing in real customer deployments. You will tailor deployments to customer needs, take workloads from proof of concept to production, and ensure performance gains deliver clear business and customer value. This leadership role combines people management, technical direction, and hands-on coding and profiling, with a customer-facing component and elements of product and technical solutions leadership.

$250k - $300k