At a Glance
- Tasks: Develop and optimise AI models for cutting-edge hardware in a dynamic environment.
- Company: Join Graphcore, a leader in AI compute backed by SoftBank Group.
- Benefits: Enjoy flexible working, generous leave, health plans, and a vibrant office culture.
- Other info: Collaborate with top minds in AI and enjoy excellent career growth opportunities.
- Why this job: Make a tangible impact on the future of AI technology with innovative projects.
- Qualifications: Strong skills in machine learning, Python/C++, and experience with large-scale systems.
The predicted salary is between 60000 - 80000 £ per year.
About Graphcore
At Graphcore, we’re building the future of AI compute. We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacentre scale. As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem. To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world. We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.
Job Summary
As a Senior Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects. Working closely with the Software development and Research teams, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology. We seek engineers with strong technical skills and an understanding of AI model implementation at scale, eager to make a tangible impact in this rapidly evolving field.
The Team
The Applied AI team’s role is to be proxies for our customers; we need to understand the latest AI models, applications, and software to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications.
If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!
Responsibilities and Duties
- Implement latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators.
- Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
- Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency.
- Design and conduct experiments on novel AI methods, implement them and evaluate results.
- Collaborate with Research, Software, and Product teams to define, build, and test Graphcore’s next generation of AI hardware.
- Engage with AI community and keep in touch with the latest developments in AI.
Candidate Profile
Essential:
- Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field.
- Proficiency in deep learning frameworks like PyTorch/JAX.
- Strong Python or C++ software development skills.
- Expertise in deep learning from model training to optimisation and evaluation.
- Experience in distributed training or inference of ML models across 64+ accelerators.
- Capable of designing, executing and reporting from ML experiments.
- Developed deep understanding of performance bottlenecks and how to overcome them.
- Ability to move quickly in a dynamic environment.
- Enjoy cross-functional work collaborating with other teams.
- Strong communicator - able to explain complex technical concepts to different audiences.
Desirable:
- Experience in one or more of: MLOps for Kubernetes-based clusters, Building production systems with large language models, Efficient computing based on low-precision arithmetic.
- Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models.
- Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies.
- Have contributed to open-source projects or published research papers in relevant fields.
- Knowledge of cloud computing platforms.
- Keen to present, publish and deliver talks in the AI community.
Benefits
In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we’re committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments. Applicants for this position must hold the right to work in the UK. Unfortunately at this time, we are unable to provide visa sponsorship or support for visa applications.
Staff Machine Learning Engineer (Large Systems) in Cambridge employer: graphcore
Contact Detail:
graphcore Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Staff Machine Learning Engineer (Large Systems) in Cambridge
✨Tip Number 1
Network like a pro! Reach out to current employees at Graphcore on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing a role in the Applied AI team.
✨Tip Number 2
Show off your skills! If you’ve worked on relevant projects, create a portfolio or GitHub repository showcasing your work with machine learning models. This can really set you apart during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on your deep learning frameworks like PyTorch or JAX. Practice coding challenges that focus on performance optimisation and distributed training.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the Graphcore team.
We think you need these skills to ace Staff Machine Learning Engineer (Large Systems) in Cambridge
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the job description. Highlight your expertise in machine learning, especially with frameworks like PyTorch or JAX, and any relevant projects you've worked on.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're excited about the role and how you can contribute to our team. Mention specific projects or experiences that demonstrate your ability to innovate in AI technology.
Showcase Your Technical Skills: Don’t shy away from detailing your technical skills in your application. Whether it's your proficiency in Python or C++, or your experience with distributed training, make sure we see how you can bring value to our projects.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen to join the Graphcore team!
How to prepare for a job interview at graphcore
✨Know Your AI Models
Make sure you’re up to speed with the latest AI models and their applications. Brush up on how these models can be optimised for performance, especially in large-scale systems. Being able to discuss specific examples will show your depth of knowledge.
✨Showcase Your Technical Skills
Prepare to demonstrate your proficiency in deep learning frameworks like PyTorch or JAX. Be ready to discuss your experience with Python or C++ and how you've applied these skills in previous projects, particularly in distributed training or inference.
✨Understand Performance Bottlenecks
Familiarise yourself with common performance bottlenecks in machine learning models. Be prepared to discuss how you’ve identified and overcome these challenges in past experiences, as this is crucial for the role at Graphcore.
✨Engage with the Community
Show your passion for AI by discussing your involvement in the AI community. Whether it’s contributing to open-source projects or attending conferences, demonstrating your engagement will highlight your commitment to staying current in the field.