Machine Learning Engineer

You’ll design, develop, and deploy real-world machine learning systems that facilitate and accelerate advanced AI solutions at scale. As part of a core product team that thrives in an “internal start up” culture, you’ll drive decision-making with engineering creativity, and build state-of-the-art analytics libraries, products, services, and tooling that solve critical client problems.

You’ve operationalized machine learning while building scalable, robust, and secure products in a commercial environment. A keen problem solver, you engineer beautiful code using the Python scientific stack, use technology to work out complex analytical problems, and have:

• been exposed to different stages of machine learning system design and development
• utilized tools for machine learning pipeline management and monitoring (for example, MLflow, Pachyderm, Kubeflow, Seldon, Grafana)
• gained familiarity with automation and deployment (CircleCI/Jenkins/github actions, etc) and infrastructure as code (Terraform, CloudFormation, etc) technologies
• employed distributed processing frameworks such as Spark and Dask, and interacted with cloud platforms and container technologies
• gained practical knowledge of software engineering concepts and best practices, like testing frameworks, packaging, API design, DevOps, DataOps and MLOps
• developed excellent problem-solving skills and easily adapt to new technologies, trends, and frameworks
• gained an advanced degree in computer science, engineering, or mathematics, or have equivalent experience

I love learning about the different business problems we solve across a range of industries. I’ve learned firsthand about airline crew planning, disaster response, satellite development, agrochemical fermentation, customer value management, and so much more. I can’t imagine another job where I would have the opportunity to work on such a variety of projects.

Deepyaman, machine learning engineer, Washington DC

You are trusted and expected to come up with what to build next. You work in small nimble teams which continuously adapt to solve the most pressing challenge. You get to build products that are immediately deployed to solve the most interesting problems.

Nikos, machine learning engineer, London