Infrastructure and MLOps Engineer Graphcore
Advanced AI systems and services for efficient machine learning.
CDI
Cambridge
Télétravail : non spécifié
Salaire : non spécifié
> 1 an
Les compétences que l’on cherche
Infrastructure as Code (IaC)
Gestion de l’infrastructure cloud
C++
Grafana
Java
+8
Votre nouvelle entreprise
517 collaborateurs
Logiciels
Intelligence artificielle / Machine Learning
L'avis de Welcome
The hardware required to train AI models comes at a high cost, both financially and environmentally, producing millions of dollars in expenses and hundreds of tons of greenhouse gases. In response, Graphcore develops accelerator chips designed to expedite AI system training while significantly reducing power consumption. AI accelerators like those crafted by Graphcore specialise in enhancing specific AI applications, such as artificial neural networks, deep learning, and machine learning. Utilising multicore designs and prioritising low-precision arithmetic or in-memory computing, these accelerators elevate the performance of large AI algorithms, achieving breakthrough results in natural language processing and computer vision. Graphcore has ambitious plans to create an AI supercomputer, aiming to be one of the most powerful in its class. However, progress on this has extended beyond its planned 2024 debut, and the company was purchased by Softbank later that year. Though the acquisition by the Japanese multinational lowered Graphcore's valuation and highlighted structural problems in the local financing of UK tech pioneers, the buyout will undoubtedly aid the company in its ambitions to shape the future of AI computing.
Vos missions au quotidien
- Join our dynamic Software Infrastructure team and take a pivotal role in scaling and managing our infrastructure. You will develop essential tools and services that empower our broader software team. Your contributions will enhance the build, test, deployment, and productisation processes of our Machine Learning Software components. Work with our High-Performance Computing (HPC) AI platforms and gain invaluable experience in distributed systems
- The Software Infrastructure team provides critical platforms and services for software development teams across the business. Our responsibilities include managing the CI platform and services, build engineering, component integration, and packaging and release systems. We operate in squads, fostering a culture of service ownership and empowerment for our engineers. We focus on long-term engineering solutions and strive to eliminate toil wherever possible
- Develop, own, and maintain tools and services to support AI research and engineering teams
- Deploy and maintain services with Kubernetes and Docker
- Manage our Cloud Infrastructure using tools such as Terraform
Benefits
- Work-Life Balance: We believe you should have a good work-life balance. Everyone’s situation is different, so we offer flexible working arrangements and start and finish times, allowing you to choose when to work from home or come into the office
- Healthcare: We offer Private Medical Insurance (PMI) to our employees to help them take care of their health and that of their loved ones
- Pension: We believe that our employees deserve the best when it comes to planning for their future
- Income Protection: Graphcore understands that unforeseen circumstances can happen, and we want to ensure our employees have the necessary financial security in place
- Progression: We prioritise learning and development as an integral part of our culture at Graphcore. Our Talent Development team is dedicated to equipping all our employees with the necessary tools, resources, and support to enhance their skills and reach their full potential in their careers
- Wellbeing: We want to ensure that our employees have the resources and support they need to prioritise their health and wellbeing
Ce que l’on cherche
- Maintaining machine learning applications
- Familiarity with cloud services (e.g. AWS)
- Understanding of CI/CD principles
- Experience managing or developing in Linux environments
- Managing ML accelerator hardware (e.g. DCGM)
- Deploying ML orchestration tools (e.g. NV Ray, KFP, SkyPilot)
- Experience using Kubernetes (k8s)
- Experience of one of the following:
- Knowledge of Python
- Knowledge of Go/Java/C++ (or similar language)
- Experience with modern observability tooling (e.g. Prometheus)
- Experience with Grafana
- Experience with GitHub Actions
- Experience with Infrastructure as Code (IaC) tools (e.g. Terraform/OpenTofu)