vLLM Jobs

Crusoe is seeking a Senior Staff Applied AI Inference Engineer to accelerate the abundance of energy and intelligence by optimizing AI infrastructure. This role focuses on making large language models run faster, cheaper, and more reliably in production by owning the inference stack end-to-end. You will be responsible for profiling performance, implementing modern optimization techniques, and diving deep into serving code. The work is applied, directly impacting customer deployments, and involves tailoring solutions to specific models, traffic patterns, latency targets, and cost constraints. You will collaborate with customer engineering teams, guide workloads from proof of concept to production, and ensure engineered gains are realized by users. This is a hands-on engineering role requiring coding, profiling, and low-level optimization, with a customer-facing component and elements of product and technical solutions work.

$250k - $300k

NVIDIA is seeking a Senior Software Engineer to specialize in Deep Learning Inference. This role involves designing, building, and optimizing GPU-accelerated software for AI applications, focusing on high-performance deep learning frameworks like SGLang and vLLM for efficient large-scale model serving and inference. You will contribute to improving these platforms, enabling the smooth deployment of advanced language models and working with the deep learning community to implement and release the latest algorithms. The work will concentrate on enhancing the performance of state-of-the-art LLM and Generative AI models across various NVIDIA accelerators, utilizing open-source tools and kernels for optimization.

$184k - $357k

Crusoe is seeking a Staff Applied AI Inference Engineer to accelerate the abundance of energy and intelligence by optimizing large language models for production environments. This role involves owning the inference stack end-to-end, focusing on profiling, applying modern optimization techniques, and deep dives into serving code to enhance speed, reduce cost, and improve reliability. The work is hands-on, involving coding, profiling, and low-level optimization, with a customer-facing component to tailor deployments to specific needs and ensure performance gains are realized in real-world scenarios. You will transition workloads from proof-of-concept to fully monitored production services, making this a core systems and performance engineering role on demanding AI workloads.

$215k - $260k

Nunchux AI is building infrastructure to make multimodal generative AI faster, cheaper, and easier to build on. Founded by MIT PhDs and CMU Professor, the company leverages deep research expertise and production systems experience. The Role focuses on bringing new image and video models to the Modelverse platform, prioritizing speed to launch. You will own the initial integration, coordinate deployment with the cloud team, and work with product engineers on API and Modelverse integration. After launch, you will hand off optimization tasks and maintain the shared code for model integration and serving.

$170k - $240k

NVIDIA is seeking a Principal Scientist to lead the technical direction for synthetic data generation across its frontier model initiatives. This role involves defining and building open-source libraries within the NVIDIA NeMo ecosystem to create synthetic datasets for text, code, structured, and multimodal data. These datasets will be crucial for the pre- and post-training of large language models like Nemotron. The position blends hands-on software engineering with applied research in generative methods, requiring collaboration with various internal teams and external research labs.

$272k - $431k

Moveworks is seeking a Machine Learning Engineer to develop cutting-edge ML infrastructure for building and serving LLMs. This role is pivotal in constructing, optimizing, and scaling end-to-end machine learning systems, encompassing distributed training and inference pipelines for large language models, model evaluation and monitoring frameworks, and LLM latency optimization. The frameworks developed will serve as a robust foundation for hundreds of ML and NLP models in production, impacting millions of enterprise employees. You will address challenges in service scalability and core algorithm optimization, working closely with machine learning, data infrastructure, and core skill teams. Your contributions will significantly influence customer AI experiences and the long-term scalability of our core AI product.

NVIDIA is seeking a motivated Deep Learning engineer to integrate advanced CUDA features and Distributed Runtime technologies into AI stacks, including frameworks like PyTorch, TRT-LLM, vLLM, SGLang, and JAX. You will join the team responsible for core CUDA features and runtimes that scale Deep Learning and HPC applications, addressing diverse multi-GPU demands from large-scale training to low-latency inference. Your work will enhance the productivity and performance of AI applications, accelerating community adoption through AI toolkits. This role offers a significant opportunity to advance the state of the art in AI technologies.

$184k - $357k