Software Engineer (Compute Platform), London

Your impact 

We are building the largest foundation models in biotech and applying them immediately to cure disease. You will play a key role and work at a grand scale to deliver the foundations that make this happen. By partnering with in-house machine learning experts and biotech researchers you will join a team to efficiently scale and plan the base on which our groundbreaking AI is built.

What you will do 

• You will focus on the end-to-end GPU/TPU (accelerator) strategy, designing infrastructure, optimizing performance, and integrating new hardware to leverage advancements. In partnership with our Machine Learning Platform team, regularly work in the environment to push and support deployments. Regularly be building, monitoring and managing cluster deployments.
• Support the technical strategy around hardware acquisition and deployment decisions
• Drive research and efficiency design around the infrastructure up to the point of service to the ML platforms teams
• Contribute to the efforts for consistently improving the reliability of our ML runs
• Operate and handle research, development, and production cloud infrastructure and systems
• Partner and collaborate with a diverse set of teams incl. science, research, product, business development and operations
• Contribute to core technical decisions (e.g. choice of tooling, infrastructure, and architectural design)

Skills and qualifications 

Essential:

• Possess real world experience of large scale AI/ML workloads
• Have experience working in cloud compute infrastructure design, preferably GCP
• Possess strong programmings skills
• Have significant experience working and deploying in Kubernetes
• Familiarity with the Nvidia GPU generations

Nice to have:

• Have a background in either ML SWE or infrastructure SRE work to build on
• Have experience leading and delivering projects to multidisciplinary stakeholders
• Familiarity with Google TPU generations
• Familiarity with: workload scheduling; machine learning efficiency research; familiarity with ML-driven R&D cycles; familiarity with hardware benchmarking