Senior ML Ops Engineer, High Performance Computing
- San Francisco, CA
Site Name: USA - Pennsylvania - Philadelphia, USA - Pennsylvania - Upper Providence, USA - California - San Francisco
Posted Date: Apr 22 2021
At GSK we see a world in which advanced applications of Machine Learning and AI will allow us to develop transformational medicines using the power of genetics, functional genomics and machine learning. AI will also play a role in how we diagnose and use medicines to enable everyone to do more feel better and live longer. It is an ambitious vision that will require the development of products at the cutting edge of Machine Learning and AI.
We're looking for a highly skilled Senior ML Ops Engineer with a focus on high-performance computing and specialized hardware to help us make this vision a reality.
Our AI/ML Platform Engineering team is building a first-in-class platform of tools and services covering high-performance computing, ML training services, DevOps, model deployment, and metadata capture; our goal is to decrease AI/ML development/training cycle time and raise the quality bar on AI/ML's shipped services by drastically simplifying the process of AI/ML Engineering. This specialist role in high-performance computing will contribute to that machine by ensuring we get the most out of our sizeable GPU fleet and other, more specialized, machine learning hardware (such as the Cerebras CS-1 ASIC), both on-premises and in the cloud.
A competitive candidate for the Senior ML Ops Engineer, High-Performance Computing role will:
- Have a proven track record of writing quality, well-documented and well-tested code for services in the AI/ML space.
- Be an expert in high-performance computing on GPUs and other specialized hardware, able to eke every last bit of performance out of the silicon whether writing low-level code or leveraging high-level frameworks like PyTorch or Tensorflow.
- Be just as comfortable building across-the-board improvements by integrating specialized hardware into DevOps / MLOps workflows and building reusable libraries as they are diving deep into the details of a specific computational project to optimize it.
The AI/ML team is built on the principles of ownership, accountability, continuous development, and collaboration. We hire for the long term, and we're motivated to make this a great place to work. Our leaders will be committed to your career and development from day one.
We are looking for professionals with these required skills to achieve our goals:
- Master's in computer science with a focus on software engineering, high-performance computing, or machine learning
- Experience and proven track-record of work in high-performance computing, DevOps / MLOps, and cloud services
- Experience with application of best practices for software engineering and high-performance computing in the AI/ML and data spaces, and experience with helping others adopt them via code reviews or similar
- Experience in software engineering with intermediate to advanced skills in Python and a particular focus on low-level details of high-performance computing
- Experience demonstrating In-depth understanding of modern ML platforms and backend systems
If you have the following characteristics, it would be a plus:
- PhD in Computer Science with a focus on software engineering, high-performance computing or machine learning
Our values and expectations are at the heart of everything we do and form an important part of our culture.
These include Patient focus, Transparency, Respect, Integrity along with Courage, Accountability, Development, and Teamwork. As GSK focuses on our values and expectations and a culture of innovation, performance, and trust, the successful candidate will demonstrate the following capabilities:
- Operating at pace and agile decision-making - using evidence and applying judgement to balance pace, rigour and risk.
- Committed to delivering high quality results, overcoming challenges, focusing on what matters, execution.
- Continuously looking for opportunities to learn, build skills and share learning.
- Sustaining energy and well-being
- Building strong relationships and collaboration, honest and open conversations.
- Budgeting and cost-consciousness
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