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Graphcore

Senior Machine Learning Engineer (Large Systems)

Graphcore is hiring a Senior Machine Learning Engineer (Large Systems) in Gdansk, Poland. Level rates it ; you can apply on Graphcore.

Level's read on this job

  • Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
  • Tools and skills the posting cites include PyTorch, JAX and MLOps.
  • Level classifies it as a senior role.
  • Level lists 196 other open roles at Graphcore.

AI in this role

PyTorchJAX
ml-opsai-research

Location: Gdánsk
Senior: PLN 260,400 - 352,200
Staff: PLN 350,700 - 474,400

About the job

Help scale state-of-the art AI models across thousands of accelerators.

As a Senior Machine Learning Engineer in the Applied AI team, you will contribute to advancing AI technology by developing and optimising new and existing AI models for our specialised hardware. You will work on large scale systems where performance is critical to the success of our projects.

Having visibility of the entire pipeline from novel accelerator hardware to state-of-the-art AI applications, and the software stack in-between, you will play a critical role in identifying opportunities to innovate and differentiate Graphcore’s technology.

Furthermore, you’ll work closely with researchers in a rapidly-evolving field, where even the most senior engineers are constantly learning and adapting to exciting new challenges.

We seek engineers with strong technical foundations who are curious and eager to understand and advance AI model implementation, at scale.

We currently have multiple opportunities available in the team at Senior and Staff level. We offer flexibility through a hybrid working model, from our modern office in Gdánsk.

If you're excited about advancing the next generation of AI models on cutting-edge hardware, we’d love to hear from you!


The team and culture

The Applied AI team’s role is to be proxies for our customers, we need to understand the latest AI models, applications, and software, as well as our own hardware and software stack, to ensure that Graphcore’s technology works seamlessly with the AI ecosystem and at scale.

Our work spans from low-level kernel development and optimisation for novel hardware through to implementing and scaling research-level algorithms for the latest AI models.

We 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.

Responsibilities

  • Implement and train state-of-the-art machine learning models and optimise for performance, accuracy and scalability across systems comprising 1000s of accelerators.
  • Benchmark and profile ML models to identify performance bottlenecks.
  • Develop deep understanding across the software stack in order to optimise kernel implementations.
  • Test and evaluate new internal software releases, provide feedback to software engineering teams, make necessary code fixes, and conduct code reviews.
  • Design and conduct experiments on novel AI methods 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.

What we’re looking for

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 hardware-accelerated deep learning from model training to optimisation and evaluation.
  • Capable of designing, executing and reporting from ML experiments.
  • Well-developed understanding of performance bottlenecks and how to overcome them.
  • Ability to move quickly in a fast-moving field.
  • 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.
  • Experience in distributed training or inference of ML models across 64+ accelerators.
  • Familiarity with HPC systems and networking including Infiniband, NVLink, RoCE technologies, and cloud computing platforms.
  • Have contributed to open-source projects or published research papers in relevant fields.
  • Keen to present, publish and deliver talks in the AI community.

Benefits

  • Flexible working: Balance your work and personal life with greater flexibility
  • Comprehensive healthcare and dental cover: Premium medical and dental insurance, with the option to extend cover to your family
  • Phantom equity: Share in Graphcore’s success
  • Fitness and wellbeing support: A fully funded Medicover gym card, giving you access to a wide range of gyms, fitness classes and leisure centres
  • Financial protection: Death and disability insurance, providing financial security for you and your loved ones
  • Retirement savings: Build your financial future, with a 4% employer contribution from Graphcore, alongside your own contributions
  • Employee Assistance Programme (EAP): Confidential advice and support for you and your family across a range of personal, financial and wellbeing matters

We welcome people from all backgrounds and experiences and are committed to building an inclusive environment where everyone can do their best work.

We’re an equal opportunity employer and recognise that everyone brings different strengths and perspectives. If you need any adjustments during the interview process, just let us know - we’re happy to support you.


Join the Team at Graphcore

Graphcore is one of the world’s leading innovators in Artificial Intelligence compute. It is developing hardware, software and systems infrastructure that will unlock the next generation of AI breakthroughs and power the widespread adoption of AI solutions across every industry.

As part of the SoftBank Group, Graphcore is a member of an elite family of companies responsible for some of the world’s most transformative technologies. Together, they share a bold vision: to enable Artificial Intelligence and ensure its benefits are accessible to everyone.

Graphcore’s teams are drawn from diverse backgrounds and bring a broad range of skills and perspectives. A melting pot of AI research specialists, silicon designers, software engineers and systems architects, Graphcore brings together deep expertise to solve complex problems and deliver meaningful progress in AI compute.

Sponsorship

Applicants must have the legal right to work in Poland. Unfortunately, we are unable to provide visa sponsorship for this role.

How we rate this

Senior Machine Learning Engineer (Large Systems) at Graphcore rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsAI ResearchPyTorchJAX

Also common in machine learning engineer jobs at this level

Pay

Typical machine learning engineer job
$199k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

  1. How do you monitor a model once it's live, and how do you know it needs retraining?
  2. Tell me about a research question you investigated. What did you find?
  3. What are the limits of PyTorch that you've run into, and how did you work around them?
  4. What's a project where you used JAX hands-on?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: ML Ops, AI Research, PyTorch, and JAX. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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