Solutions Engineer

About the role

As a Solutions Engineer, you will work closely with our current and prospective customers as the primary technical resource for the field sales force. You will own the technical sales process from introductory meetings and demos to proofs of concepts and customer success handoff.

You will assist in progressing the sales process, working in conjunction with the sales team, while building strong relationships with our customers’ technical staff. The Solution Engineer must be able to articulate technology and product positioning to both business and technical users. You must be able to assist prospective customers through proofs of concepts, identify and resolve technical issues and involve the Applied Machine Learning team when necessary in order to successfully complete customer engagements.

Requirements

  • Minimum of 3 years of sales engineering experience selling deeply technical enterprise software products
  • Excellent communication and presentation skills, both written and verbal
  • Strong problem solving and analytical skills
  • Experience with data engineering, using and/or debugging open-source libraries used in enterprise infrastructure solutions such as Docker, Kubernetes, Mesos, Hadoop / HDFS, and Apache Spark
  • Programming proficiency in a language like Python, Java or Scala and eagerness to help customers who are primarily users of Python Deep Learning frameworks be successful
  • Experience with Linux/Unix

Preferred

  • Bachelors, Masters or PhD in Computer Science, Machine Learning, Statistics, Math, or equivalent deep theoretical knowledge of machine learning algorithms
  • Previous professional experience as a software engineer
  • Experience with and/or deep knowledge of machine learning
  • Proficiency with one or more of the leading deep learning software packages: TensorFlow, Keras, PyTorch, or MXNet

Teams & Process

We are building a team of world class engineers — join us! We have one product and one team, where everyone is a worker-leader. We combine input from customers, engineers and company leadership to prioritize our work, and work hard to make decisions transparent. We believe in tight feedback with customers, and in minimum valuable products.

We believe in just enough (but not too much) process; currently we run scrum with two week sprints. We use Github to manage our work; we require code review, lint, and tests to pass for all our PRs. We run an extensive continuous integration pipeline to test our GPU features. We use Slack, GSuite and have provisioned a video conferencing system for our remote workers.

Technical Challenges

We have implemented, from scratch, a distributed, fault tolerant GPU cluster manager and scheduler, purpose-built for DL and ML workloads. We have invented, published and implemented state-of-the-art hyperparameter optimization algorithms in our platform. We have numerous other research ideas ready to turn into product features that will differentiate us from our competitors.

Technical Stack

    Go

    Python

    Docker

    TensorFlow

    PyTorch

    Keras

    Elm

    Kubernetes

    Mesos

    PostgreSQL