Machine Learning Engineer - Application Software

Who You AreWe are seeking talented Machine Learning Systems Engineers to contribute to the development of the world's largest end-to-end 3D native machine learning systems. You will collaborate on our comprehensive ML framework tailored for 3D applications, encompassing pretraining, fine-tuning, inference, and more. We value strong hands-on engineering skills, a passion for learning, and an ability to excel in a dynamic, high-responsibility environment.Who We AreAt Meshy, we envision a world where 3D creation is limitless and accessible to all. Our mission is clear: unleash creativity. We have developed a comprehensive pipeline for 3D content that spans text/image to 3D, texturing, texture editing, animation rigging, and beyond. Additionally, we foster a vibrant community for creators to share their work, draw inspiration from others, and utilize our platform as an asset marketplace for their games and prototypes. Recognized as the No.1 in popularity among 3D AI tools (according to the 2024 A16Z Games survey), Meshy delivers real value to enterprises such as Meta, Square Enix, and DeepMind, as well as millions of end-users. Our technology powers game and film production, 3D printing, industrial product design, user-generated content features, and even training simulations for robotics and physical AI.Your Next Challenge3D is the exciting new frontier of Generative AI, and your role at Meshy will present unique challenges in both training and inference. You will engage with the full stack of AI, from debugging and monitoring hardware platforms, building training frameworks, scaling high-throughput 3D data pipelines, collaborating with researchers on novel model architectures, to developing efficient inference engines for diffusion models and more. Here are some specific challenges on the training side:Collaborate closely with researchers to co-design the next frontier of 3D & Spatial AI.Develop and refine modern PyTorch solutions for maximum parallelism and efficiency, establishing a clean and intuitive training infrastructure for our foundational models.Identify bottlenecks and optimize for high throughput & efficient distributed model training across hundreds to thousands of GPUs.Implement and maintain 3D-specific custom operators in Triton or CUDA.Design and uphold novel data-loading frameworks and libraries.

About the Institute of Foundation ModelsWe are a pioneering research laboratory focused on the development, understanding, application, and risk management of foundational models. Our mission is to propel research forward, cultivate the next generation of AI innovators, and make substantial contributions to a knowledge-driven economy.Join us and collaborate with top-tier researchers, data scientists, and engineers on the forefront of foundational model training. Engage in solving critical challenges that can redefine entire sectors through advanced AI solutions. Your strategic and innovative problem-solving skills will play a vital role in positioning MBZUAI as an international leader in high-performance computing for deep learning, facilitating discoveries that will inspire future AI trailblazers.The Role We are seeking a skilled distributed ML infrastructure engineer to enhance and expand our training systems. You will collaborate closely with distinguished researchers and engineers to:• Develop and scale distributed training frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod)• Implement distributed optimizers based on mathematical specifications• Create robust configuration and launching systems across multi-node, multi-GPU clusters• Manage experiment tracking, metrics logging, and job monitoring for enhanced external visibility• Enhance the reliability, maintainability, and performance of training systems• While much of your work will support large-scale pre-training, prior pre-training experience is not mandatory; strong infrastructure and systems expertise are our primary focus.Key Responsibilities • Distributed Framework Ownership – Extend or adapt training frameworks (e.g., DeepSpeed, FSDP) to accommodate new applications and architectures.• Optimizer Implementation – Convert mathematical optimizer specifications into distributed implementations.• Launch Config & Debugging – Develop and troubleshoot multi-node launch scripts with adaptable batch sizes and parallelism strategies.

At Bee Genius, we are pioneering the future of work today with innovative AI solutions that transform industries.Job Overview: We are looking for a talented AI/Machine Learning Engineer to become a vital part of our dynamic team. In this role, you will leverage your expertise to develop and deploy cutting-edge machine learning models and algorithms aimed at addressing complex business challenges.Key Responsibilities:Design, build, and refine machine learning models and algorithms.Train and assess models using extensive datasets.Optimize models for enhanced performance and accuracy.Collaborate with data scientists and software engineers to integrate models into operational systems.Stay abreast of the latest trends in AI and machine learning technologies.Promote the ethical deployment of AI solutions.

Cerebras Systems is at the forefront of AI innovation, creating the world’s largest AI chip, which is 56 times larger than traditional GPUs. Our groundbreaking wafer-scale architecture delivers the computational power equivalent to dozens of GPUs on a single chip, combined with the programming simplicity of a unified device. This innovative approach allows us to offer unparalleled training and inference speeds, enabling machine learning practitioners to execute extensive ML applications seamlessly, without the complexities of managing multiple GPUs or TPUs.Cerebras boasts an impressive clientele, including premier model labs, global corporations, and pioneering AI startups. Recently, OpenAI announced a multi-year partnership with Cerebras, aimed at deploying 750 megawatts of scale, revolutionizing critical workloads with ultra-fast inference capabilities.Our unique wafer-scale architecture enables Cerebras Inference to provide the fastest Generative AI inference solution globally, surpassing GPU-based hyperscale cloud inference services by more than tenfold. This remarkable enhancement in speed is reshaping the AI application user experience, facilitating real-time iteration and boosting intelligence through enhanced computational capabilities.About The RoleThe Inference ML Engineering team at Cerebras Systems is committed to empowering our rapid generative inference solution through intuitive APIs, supported by a distributed runtime that operates on extensive clusters of our proprietary hardware. Our goal is to enable enterprises, developers, and researchers to fully harness the capabilities of our platform, leveraging its exceptional performance, scalability, and flexibility. The team collaborates closely with cross-functional groups, including compiler developers, cluster orchestrators, ML scientists, cloud architects, and product teams, to deliver impactful solutions that redefine the limits of ML performance and usability.As a Senior Software Engineer on the Inference ML Engineering team, you will be instrumental in designing and implementing APIs, ML features, and tools that facilitate the execution of state-of-the-art generative AI models on our custom hardware. Your role will involve architecting solutions that allow for seamless model translation and execution, ensuring high throughput and minimal latency while maintaining user-friendliness. You will lead technical initiatives and collaborate with other engineering teams to enhance our solutions.

About the Institute of Foundation ModelsWe are an innovative research laboratory focused on the creation, comprehension, application, and risk management of foundation models. Our mission is to propel research forward, cultivate the next generation of AI innovators, and contribute significantly to a knowledge-driven economy.Joining our team presents a unique opportunity to engage in the core of advanced foundation model training, collaborating with leading researchers, data scientists, and engineers as we address the most pivotal and influential challenges in AI advancement. Your work will involve the creation of groundbreaking AI solutions with the potential to revolutionize entire industries. Employing strategic and innovative problem-solving skills will be crucial in establishing MBZUAI as a premier global center for high-performance computing in deep learning, fostering remarkable discoveries that inspire future AI trailblazers.

Cerebras Systems is at the forefront of AI technology, creating the world's largest AI chip that is 56 times the size of traditional GPUs. Our innovative wafer-scale architecture combines the compute power of dozens of GPUs into a single chip, simplifying the programming experience. This unique design enables us to achieve unparalleled training and inference speeds, allowing machine learning practitioners to run extensive ML applications seamlessly without the complexities of managing numerous GPUs or TPUs.Our clientele includes premier model laboratories, multinational corporations, and pioneering AI-driven startups. Notably, OpenAI has recently formed a multi-year collaboration with Cerebras, aiming to harness 750 megawatts of computational scale to revolutionize key workloads through ultra-high-speed inference.Thanks to our cutting-edge wafer-scale architecture, Cerebras Inference delivers the fastest Generative AI inference solution globally, achieving speeds over ten times faster than GPU-based hyperscale cloud inference services, thus transforming the user experience of AI applications and enabling real-time iterations and enhanced intelligence through additional agentic computation.Responsibilities:Lead the design and implementation of advanced system-level debugging, validation, and observability platforms.Develop automated systems for collecting and analyzing numerical data and execution anomalies.Create visualization and analysis tools to facilitate efficient root-cause investigations.Build frameworks for failure classification, regression detection, and anomaly monitoring.Enhance compilers, runtimes, and programming interfaces to support sophisticated profiling and instrumentation.Improve workflows related to system bring-up, low-level debugging, and validation.Collaborate cross-functionally with teams in compiler, hardware, firmware, runtime, and infrastructure domains.Establish best practices to ensure debuggability, reliability, and operational excellence.Lead impactful initiatives and support incident response while driving long-term corrective solutions.

Cerebras Systems is at the forefront of AI innovation, having developed the world's largest AI chip, which is 56 times greater in size than conventional GPUs. Our revolutionary wafer-scale architecture delivers the computational power of multiple GPUs on a single chip, simplifying programming to a single device experience. This unique approach enables Cerebras to provide unparalleled training and inference speeds, allowing machine learning professionals to seamlessly operate large-scale ML applications without the complexities of managing numerous GPUs or TPUs.Our clientele includes leading model labs, global corporations, and pioneering AI-native startups. Recently, OpenAI formed a multi-year collaboration with Cerebras to harness 750 megawatts of capacity, revolutionizing critical workloads with ultra-fast inference capabilities.Thanks to our innovative wafer-scale architecture, Cerebras Inference stands as the fastest Generative AI inference solution globally, boasting speeds over ten times faster than traditional GPU-based hyperscale cloud inference services. This significant enhancement in speed transforms user experiences with AI applications, facilitating real-time iterations and augmenting intelligence through additional agentic computation.About The RoleIn the capacity of a Senior Software Engineer within the ML Integration and Quality team, you will be instrumental in integrating and delivering all software and hardware components of the Cerebras AI platform. Your focus will be on software feature integration and quality assurance, including pre-deployment and production validation of Cerebras' training and inference solutions. You will advocate for superior testing practices, effective debugging methodologies, and exemplary cross-team communication to ensure the delivery of world-class products.

Illumio builds technology to contain ransomware and security breaches, helping organizations defend against cyber threats. The Illumio AI Security Graph underpins a platform that spots and contains threats in hybrid multi-cloud setups, aiming to stop attacks before they spread. Illumio is recognized as a leader in microsegmentation and supports Zero Trust architectures for critical infrastructure. The engineering team focuses on advancing cybersecurity through leadership, autonomy, and a strong sense of ownership. Engineers here develop and maintain a scalable SaaS platform using cloud-native tools, with deployments in both cloud and on-premises environments. Precision, quality, and collaboration shape the team's work, and engineers are encouraged to take initiative at every level. This Senior Machine Learning Engineer role is based onsite at Illumio’s Sunnyvale headquarters. The position centers on designing and scaling systems that power Illumio’s AI-driven security platform. Work involves handling large-scale data, distributed systems, and building advanced AI agents. Key Responsibilities Design and optimize high-throughput, event-driven systems with Apache Kafka to support real-time data flows. Develop and maintain large-scale data pipelines using Apache Spark or Flink for high-volume analytics and AI features. Create advanced AI agents that handle autonomous planning, memory management, and reliable tool use in distributed environments. Lead architectural design for containerized services on Kubernetes, focusing on availability and scalability across cloud platforms such as AWS, Azure, and GCP.

Cerebras Systems is at the forefront of AI technology, having developed the world's largest AI chip, which is 56 times larger than traditional GPUs. Our innovative wafer-scale architecture delivers the computational power equivalent to dozens of GPUs on a single chip while maintaining the programming simplicity of a single device. This unique approach enables Cerebras to provide unparalleled training and inference speeds, allowing machine learning practitioners to seamlessly run large-scale ML applications without the complexities of managing numerous GPUs or TPUs. Cerebras proudly serves a diverse clientele, including leading model labs, global enterprises, and pioneering AI-native startups. Notably, OpenAI has recently formed a multi-year partnership with Cerebras to harness 750 megawatts of scale, revolutionizing key workloads with ultra high-speed inference. Our groundbreaking wafer-scale architecture ensures that Cerebras Inference stands as the world's fastest solution for Generative AI inference, achieving speeds over ten times faster than GPU-based hyperscale cloud inference services. This remarkable increase in speed is transforming the user experience of AI applications, enabling real-time iterations and enhancing intelligence through additional agentic computation.About The RoleCerebras is expanding its Machine Learning team to spearhead a new initiative that aligns with our existing teams. We are seeking a Principal Investigator to collaborate with our ML leaders in shaping this new effort while building the team and enhancing our capabilities. This new team will work in concert with our current ML divisions: Field ML, which directly engages with customers, Applied ML, which develops new ML capabilities and applications, and Core ML, which adapts ML algorithms to leverage the unique features of Cerebras hardware. The new team may undertake similar or complementary responsibilities.The new team will focus on areas such as:Post-training and reinforcement learning: Enhancing model deployment quality through advanced training, tuning, and reinforcement learning techniques, concentrating on specific downstream tasks;Dataset curation and optimization: Implementing strategies to gather and select high-quality data, facilitating quicker and higher-quality model training and tuning;LLM Pretraining: Engaging in...

About Applied IntuitionApplied Intuition, Inc. is at the forefront of advancing physical AI technology. Established in 2017 and currently valued at $15 billion, this Silicon Valley-based company is building the essential digital infrastructure to infuse intelligence into every moving machine worldwide. We cater to industries such as automotive, defense, trucking, construction, mining, and agriculture through three primary sectors: tools and infrastructure, operating systems, and autonomy. Our solutions are trusted by 18 of the top 20 global automakers, along with the United States military and its allies, to deliver exceptional physical intelligence. Our headquarters is located in Sunnyvale, California, with additional offices across Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Discover more at applied.co.We are an in-office company, expecting our employees to primarily work from their Applied Intuition office five days a week. We understand the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments.About the RoleWe are in search of a skilled software engineer with extensive experience in optimizing machine learning models and deploying them in production-grade embedded runtime environments. Your expertise will span the entire ML framework stack, including PyTorch, JAX, ONNX, TensorRT, CUDA, XLA, and Triton.At Applied Intuition, You Will:Lead ML performance optimization across various technologies for both on-road and off-road ADAS/AD stacks aimed at deployment on a range of embedded computing platforms.Devise compute usage strategies to enhance efficiency and minimize latency of model inference for compute boards chosen by our customers.Engage in model pruning and quantization, ensuring successful deployment on memory-constrained platforms.Collaborate closely with ML engineers and software developers to identify and optimize efficient model architecture solutions.Establish methodologies to...

About Applied IntuitionApplied Intuition, Inc. is at the forefront of physical AI innovation. Established in 2017 and currently valued at $15 billion, this Silicon Valley firm is developing the essential digital infrastructure required to integrate intelligence into every moving machine globally. Serving vital sectors such as automotive, defense, trucking, construction, mining, and agriculture, Applied Intuition focuses on three primary areas: tools and infrastructure, operating systems, and autonomy. Our solutions are trusted by 18 of the world's top 20 automakers, along with the United States military and its allies, to deliver transformative physical intelligence. With headquarters in Sunnyvale, California, we boast additional offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Discover more at applied.co.We embrace a culture of in-office collaboration, with a primary expectation for our team members to work from the Applied Intuition office five days a week. However, we value flexibility and trust our employees to manage their schedules responsibly. This includes the possibility of occasional remote work, starting the day with morning meetings from home, or leaving early to accommodate family obligations.About the RoleThe Data & Machine Learning Pipeline Engineer will play a crucial role in Applied Intuition's data flywheel initiative, developing systems that link vehicle data collection, training, and automated model enhancement. You will establish the infrastructure that enables our autonomous driving stack to learn continuously from both real-world and simulated data, thereby accelerating development across teams focused on perception, planning, and control.This position resides at the intersection of large-scale data engineering and machine learning infrastructure. You will collaborate closely with ML engineers and system developers to automate processes related to data selection, curation, and model iteration, ensuring our vehicles can improve autonomously with minimal human input.

We are seeking a dynamic and experienced Manager for our AI and Machine Learning team at LinkedIn. In this role, you will lead a talented group of engineers and data scientists dedicated to developing cutting-edge solutions that enhance user experience and drive engagement across the platform. Your leadership will be crucial in shaping the direction of our AI initiatives, ensuring they align with our mission to connect the world's professionals.The ideal candidate will possess a strong background in machine learning algorithms, data analysis, and software development, as well as exceptional communication skills to effectively collaborate with cross-functional teams. If you are passionate about leveraging AI to create impactful solutions, we want to hear from you!

At Cerebras Systems, we are at the forefront of AI technology, developing the world's largest AI chip that is 56 times larger than conventional GPUs. Our innovative wafer-scale architecture enables the computational power of dozens of GPUs on a single chip, simplifying programming to the ease of handling one device. This unique design allows us to achieve unparalleled training and inference speeds, empowering machine learning practitioners to seamlessly deploy large-scale ML applications without the complexity of managing numerous GPUs or TPUs.Our clientele includes leading model labs, global enterprises, and pioneering AI-native startups. Recently, OpenAI announced a multi-year partnership with Cerebras aimed at leveraging 750 megawatts of scale to revolutionize critical workloads through ultra-high-speed inference.Thanks to our groundbreaking wafer-scale architecture, Cerebras Inference delivers the fastest Generative AI inference solution globally, exceeding GPU-based hyperscale cloud inference services by over tenfold. This significant boost in speed is transforming the user experience of AI applications, facilitating real-time iteration and enhancing intelligence through added agentic computation.About The RoleAs an Applied Machine Learning Research Scientist at Cerebras, you will be instrumental in converting modern machine learning methodologies into scalable, high-performance systems. This position focuses on the intersection of modeling and systems, emphasizing the efficient execution of existing algorithms rather than merely publishing new ones. Your efforts will significantly influence the training, optimization, and deployment of large language models (LLMs) on one of the most sophisticated AI platforms in existence.You will collaborate closely with fellow researchers and senior engineers to enhance workflows for LLM pretraining, fine-tuning, and reinforcement learning-based post-training. Your responsibilities will encompass building training pipelines, debugging complex system behaviors, improving model quality, and refining data and evaluation strategies. Your contributions will have a direct and meaningful impact on advancing our capabilities in AI.

Join our innovative team at intuitive as a Machine Learning Engineer, where you'll have the chance to work on cutting-edge AI technologies that are shaping the future. In this role, you will design, develop, and implement machine learning models that will drive impactful solutions across various sectors.As a critical member of our team, you will collaborate with data scientists and engineers to enhance our product offerings, ensuring they are not only effective but also scalable. This is an exceptional opportunity for those eager to leverage their skills in a thriving environment.

About Applied IntuitionApplied Intuition, Inc. is at the forefront of advancing physical AI technologies. Established in 2017 and currently valued at $15 billion, this Silicon Valley powerhouse is building the essential digital infrastructure to infuse intelligence into every moving machine on Earth. Serving a diverse range of industries including automotive, defense, trucking, construction, mining, and agriculture, Applied Intuition excels in three key domains: tools and infrastructure, operating systems, and autonomy. Eighteen of the world's top 20 automakers, along with the United States military and its partners, rely on our solutions to deliver physical intelligence. Our headquarters is located in Sunnyvale, California, with additional offices in Washington, D.C.; San Diego; Ft. Walton Beach, FL; Ann Arbor, MI; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Discover more at applied.co.As an in-office company, we expect our employees to primarily work from their Applied Intuition office five days a week. However, we value flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home, or leaving early for family commitments.Role and Team OverviewWe seek a dedicated Research Engineer (AI/RL Infrastructure) to join our Research Group at Applied Intuition. This position is perfect for engineers who design, build, and maintain large-scale machine learning systems and collaborate closely with researchers to innovate and enhance the foundational platform for next-generation physical AI systems.The Research Group's mission is to develop pioneering technologies that facilitate next-generation physical AI, focusing on two of the most challenging applications that are transforming our daily lives: end-to-end autonomous driving and robotic generalists. Our team comprises leading experts from prestigious institutions and organizations, recognized for their outstanding contributions in both academia and industry, including multiple Best Paper awards at top conferences and journals such as CVPR and ICRA. For further insights, visit appliedintuition.com/research.

Join our dynamic team as a Senior Machine Learning Engineer at Intuitive, where you will play a pivotal role in advancing our robotic surgery technologies. We are looking for a talented individual with a strong background in machine learning, artificial intelligence, and data analysis.

About Applied IntuitionApplied Intuition, Inc. is at the forefront of revolutionizing physical AI. Founded in 2017 with a valuation of $15 billion, this Silicon Valley innovator is developing the essential digital infrastructure required to infuse intelligence into every moving machine globally. Our services cater to the automotive, defense, trucking, construction, mining, and agriculture sectors across three core domains: tools and infrastructure, operating systems, and autonomy. Trusted by 18 of the top 20 global automakers, as well as the U.S. military and its allies, our solutions are designed to deliver superior physical intelligence. Headquartered in Sunnyvale, California, we also have offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Discover more at applied.co.At Applied Intuition, we are committed to fostering an in-office culture, requiring our employees to primarily work from the office five days a week. However, we value flexibility and trust our employees to responsibly manage their schedules. This may include occasional remote work, starting the day with morning meetings from home, or leaving early to accommodate family commitments.Meet our software engineers!Get to know some of our software engineers who are pioneering the future of autonomy and delivering top-notch solutions that help customers reduce time to market. Learn what motivated them to join Applied Intuition, what keeps them engaged, and their insights for prospective candidates.About the roleWe are seeking a talented software engineer to join our team focused on integrating advanced machine learning methodologies into high-quality sensor simulation. In this role, you will collaborate with our research team to implement cutting-edge techniques for modeling environments and sensors, including Lidars, Radars, and Cameras.At Applied Intuition, you will:...

About the Institute of Foundation ModelsWe are a pioneering research laboratory focused on developing, understanding, utilizing, and managing foundation models. Our mission is to propel research, cultivate the next generation of AI innovators, and create transformative impacts within a knowledge-driven economy.Join our dynamic team and seize the opportunity to engage in groundbreaking foundation model training, collaborating with elite researchers, data scientists, and engineers to address the most pressing challenges in AI development. You will contribute to the creation of innovative AI solutions with the potential to revolutionize industries. Your strategic and creative problem-solving abilities will play a crucial role in establishing MBZUAI as a global center for high-performance computing in deep learning, fostering discoveries that will motivate future AI trailblazers.The RoleAs a Machine Learning Engineer at the Institute of Foundation Models, your main duty will be to design and implement cutting-edge machine learning models that tackle real-world issues, pushing the limits of artificial intelligence research. You will work collaboratively with diverse teams to deploy scalable solutions, furthering MBZUAI’s goal of driving significant AI advancements and solidifying the institution’s status as a leader in the international AI research community. Your expertise will be vital in enhancing the performance of large-scale machine learning models and aiding in the development of transformative AI tools that can reshape industries globally.

Cerebras Systems revolutionizes the AI landscape with the creation of the world’s largest AI chip, a remarkable 56 times larger than conventional GPUs. Our innovative wafer-scale architecture delivers the computational power of numerous GPUs on a single chip, simplifying programming efforts for users. This unique approach enables Cerebras to achieve unparalleled training and inference speeds, empowering machine learning practitioners to seamlessly execute large-scale ML applications without the complexities of managing hundreds of GPUs or TPUs.Our clientele includes leading model laboratories, global enterprises, and pioneering AI-native startups. Notably, OpenAI recently announced a multi-year partnership with Cerebras to deploy 750 megawatts of scale, significantly enhancing key workloads with ultra-high-speed inference.Thanks to our groundbreaking wafer-scale architecture, Cerebras Inference provides the fastest Generative AI inference solution globally, exceeding the performance of GPU-based hyperscale cloud inference services by over ten times. This significant speed enhancement transforms the user experience of AI applications, facilitating real-time iterations and augmented intelligence through additional agentic computation.About The RoleWe are on the lookout for a highly skilled and experienced AI Infrastructure Operations Engineer to oversee and manage our state-of-the-art machine learning compute clusters. In this role, you will have the unique opportunity to work with the world’s largest computer chip, the Wafer-Scale Engine (WSE), and the systems that leverage its extraordinary power.You will play a pivotal role in ensuring the health, performance, and availability of our infrastructure, maximizing compute capacity, and supporting our expanding AI initiatives. This position requires an in-depth understanding of Linux-based systems, expertise in containerization technologies, and experience in monitoring and troubleshooting complex distributed systems. The ideal candidate is a proactive problem-solver with a strong background in large-scale compute infrastructure who is reliable and committed to customer success.