ML Lead

ML Lead

About You

You are a generalist with strong understanding of machine learning and experience turning prototypes into products. You are curious and enjoy experimenting with new tools and technology. You communicate complex ideas simply and effectively, and love collaboration.

You will have industry experience working on a range of recommender, machine translation, and related optimization systems. You are interested in applying this experience to developer tools.

You will bring the expertise needed to manage a machine learning lifecycle alongside experienced engineers building AI-powered software for running software.

Role

• Use your machine learning expertise to build products and features for the people that run and operate software

• Come up with original ideas that machine learning can provide value to our users, and rapidly prototype these ideas

• Lead a team from a technical perspective to establish machine learning best practices and influence software engineering culture

• Stay updated with emerging technologies, tools, and practices in the ML field. A strong understanding of ML libraries is one of your strengths. You’ll recommend specific tools and explain why they are the right solution.

• Rapid prototyping and iterative development will be your default methods for testing hypotheses and refining solutions.

• Lead data-related tasks, including data mining and cleaning, to ensure the quality and reliability of our data sources.

• Use standard machine learning methods to best use modern distributed and parallel environments.

Skills

• Knowledge of writing and debugging Python and knowledge of popular machine learning frameworks like PyTorch and JAX.

• Experience in one or more of the following areas: machine translation, recommender systems, natural language processing, or related machine learning field.

• Knowledge of web development patterns—we use Django but any experience with MVC frameworks is good

• Super communication skills— you’ll need to convey your ideas clearly to the team and the public

• Exposure to popular architectural patterns of scalable, data-driven applications