Machine Learning Engineer - NLP


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Job's description

Gantry is building infrastructure and developer tools to help machine learning teams turn their projects into production-grade machine learning systems. We offer an Evaluation Store, a single source of truth for model performance metrics. Our tool helps machine learning teams understand the performance of their models in production and make operational decisions like whether to deploy, when to retrain, and what data to label and retrain on.
Gantry was founded by Josh Tobin, former OpenAI researcher and co-creator of Full Stack Deep Learning, and Vicki Cheung, former founding engineer at OpenAI and Compute team lead at Lyft. Gantry is well-funded and backed by top-tier venture capital firms and angel investors including Pieter Abbeel and Greg Brockman.
We value diversity and encourage candidates from underrepresented groups to apply.

We are hiring a machine learning engineer focused on NLP to join our core team. Since we are building tools for ML engineers, this is a dynamic role that will involve both coming up with pragmatic solutions to challenging ML problems and helping develop a user experience that is delightful for people who spend their time developing models.

What you'll do

  • Develop and implement the core algorithms powering our product
  • Collaborate with our software engineers to design abstractions and APIs that are flexible and delightful to use for ML practitioners
  • Keep up with state-of-the-art knowledge in applied NLP
  • Extend our libraries to support a variety of NLP tasks
  • Develop algorithms and techniques to help our test their models and perform error analysis
  • Set engineering standards and culture as we grow the team

You may be a good fit for this role if you

  • Have experience deploying modern NLP models in production, or have research experience in NLP, ML Systems, or Explainable/Interpretable ML
  • Want to learn about how a wide range of companies are using ML in production, and are excited to accelerate their ability to do so
  • Enjoy working on machine learning problems that don't have a clear cut best-practice solution
  • Care deeply about the tools that ML engineers use to do their jobs
  • Want to have a large impact as an early member of the team
Most of the team is based in San Francisco, but we are building a remote-friendly company and welcome applicants from anywhere in the US. We offer competitive salary, a significant equity stake in the company, and full benefits

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