Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyse, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know diverse backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.
As an ML Systems Engineer, you will play a fundamental role in building our state-of-the-art machine learning platform. You will get the chance to tackle challenging problems at the cutting edge of machine learning research and development and to collaborate with leading machine learning researchers and engineers.Your goal is to make seamless software for deep learning research at scale! You will have the opportunity to define major aspects of our product: you’ll be expected to take on difficult problems without clear solutions and to design, build, and iterate until we’ve reached elegant solutions that delight our customers.You will work on a wide range of problems, from designing and implementing libraries and APIs to developing and optimizing applications enabling inference using, and fine-tuning of, state-of-the-art deep learning models on modern accelerators and compute clusters.Requirements
- Strong problem solving and analytical skills
- Excellent communication skills, both written and verbal
- Strong Python programmer
- Extensive experience with numerical computing in Python, e.g., NumPy, PyTorch, or TensorFlow
- Experience building, designing, and documenting libraries/APIs
- Experience developing with containerization framework (e.g., Docker, Singularity)
- Excited about building systems to enable machine learning at scale
- NOTE: knowledge of machine learning or statistics is not required
Preferred
- Familiarity with modern deep learning software ecosystems
- Familiarity with distributed and concurrent computing
- Familiarity with hardware performance, HPC, and/or scientific computing, e.g., CUDA, MPI
- Graduate degree (or bachelor’s degree with relevant experience) in computer science or a related field
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Additional Skills:
- Cloud Architectures, Cross Domain Knowledge, Design Thinking, Development Fundamentals, DevOps, Distributed Computing, Microservices Fluency, Full Stack Development, Security-First Mindset, Solutions Design, Testing & Automation, User Experience (UX)
