We are looking for a talented and highly motivated ML Engineer to join our team. ML Engineers are responsible for building systems at the crossroads of data science and distributed computing. You will do a little bit of everything: from tuning machine learning models, to profiling distributed applications, to writing highly scalable software. We use technologies like Kubernetes, Docker, Kafka, gRPC, and Spark. You aren’t a DevOps, but an understanding of how the nuts and bolts of these systems fit together is helpful and you aren't a data scientist, but understanding how models work and are applied is just as important.

U.S. Citizenship Required Responsibilities

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  Collaborate with data scientists to get their models deployed into production systems.
  


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  Develop and maintain systems for distributed model training and evaluation.
  


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  Design and implement APIs for model training, inference, and introspection.
  


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  Build tools for testing, benchmarking, and deploying analytics at scale.
  


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  Interface with the technical operations team to understand analytic performance and operational behavior.
  


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  Write and test code for highly available and high volume workloads.
  

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  BS or Master’s degree in Computer Science, related degree, or equivalent experience.
  


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  5+ years experience with software engineering, infrastructure design, and/or machine learning.
  


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  Familiarity with Python and machine learning frameworks, paricularly Scikit-learn, Tensorflow, and Pytorch.
  


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  Experience with distributed machine learning using tools like Dask, Tensorflow, Kubeflow, etc.
  


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  Write well-structured, maintainable, idiomatic code with good documentation.
  


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  Strong work-ethic and passion for problem solving.
  

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  Machine learning API development competencies.
  


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  Golang development experience.
  


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  Container orchestration and optimization knowledge.
  


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  Proficiency designing, implementing, and operating large-scale distributed systems.
  


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  Prior experience working in a distributed (fully remote) organization.