As a Systems Engineer, you will play a fundamental role building our core deep learning platform. You’ll get the chance to tackle challenging problems at the cutting edge of deep learning research and development, and to collaborate with leading machine learning researchers and engineers.
You will have the opportunity (and responsibility!) to define major aspects of our product: you’ll be expected to take on a difficult problem without a clear solution, and to design, build, and iterate until we’ve reached an elegant solution that delights our customers. You will work on problems such as efficient cluster scheduling over heterogeneous GPUs, implementing cutting-edge algorithms for hyperparameter optimization, and designing systems for managing ETL pipelines and automated deployment of deep models.
- Strong problem solving and analytical skills
- Excellent communication skills, both written and verbal
- An exceptional track record of designing, implementing and shipping scalable, reliable production-quality software
- Experience with distributed and/or concurrent software development
- Theoretical knowledge of statistics or machine learning is not required
- Experience building systems for large-scale data management, analytics, cluster scheduling, stream processing, or machine learning
- Familiarity with modern container-based cluster managers (e.g., Kubernetes, DC/OS)
- Experience doing operations and being on-call for production systems
- Interest or experience in machine learning and/or deep learning
- Familiarity with hardware performance, HPC and/or scientific computing
We use two-week engineering sprints to strike the right balance between execution and evaluating priorities. We structure our collaboration around projects: The project lead is an individual contributor directly responsible for the execution of the project, including planning the technical solution, coordinating work with other engineers, and communicating progress to stakeholders. Engineering managers ensure that project leads at all levels of their career have the support and guidance they need to fulfill these responsibilities effectively.
All code is reviewed by members of the team, following a specific set of guidelines to reduce ambiguity and drive to shipping efficiently. You can check out how we review code on our public repository.
To apply for this job please visit jobs.lever.co.