Staff Machine Learning Engineer

ALL SOURCEGRAPH ROLES ARE FULLY REMOTE

Who we are

Our mission at Sourcegraph is to make it so that everyone can code, not just ~0.1% of the population. Our code graph powers Cody, the most powerful and accurate AI coding assistant, as well as our Code Search product, which helps devs explore their entire codebase and make large-scale migrations and security fixes. We’re building software that builds software, and in doing so we’re making devs more productive and preparing for a world where a lot more code gets written.

It’s an exciting time to join Sourcegraph. AI has taken over the world, and we’ve spent the last 10 years building infrastructure that’s integral to making AI generated code more powerful and accurate. Our customers include 4/5 FAANG companies, 4 of the top 10 banks, government organizations, Uber, Plaid, and many other companies building the software that pushes the world forward. We’ve raised $225M at a $2.625B valuation from Andreessen Horowitz, Sequoia, Redpoint, Craft and others. We’re making ambitious bets on our future and we’re looking to hire exceptional people to join our team as we make Sourcegraph one of the biggest and most influential companies in the world.

Working hours

🌎 Given that we are an all-remote company and hire almost anywhere in the world, we don’t have a particular time-zone preference for this role. However, you may need to be available for non-recurring urgent meetings outside of working hours.

Why this job is exciting

We are creating a machine learning team at Sourcegraph, aimed at creating the most powerful coding assistant in the world. Many companies are trying, but Sourcegraph is uniquely differentiated by our rich code intelligence data and powerful code search platform. In the world of prompting LLMs, context is everything, and Sourcegraph’s context is simply the best you can get: IDE-quality, global-scale, and served lightning fast. Our code intelligence, married with modern AI, is already providing a remarkable alpha experience, and you can help us unlock its full potential.

We are looking for an experienced full stack ML engineer with demonstrated industry experience in productionizing large scale ML models in industrial settings. And if you happen to have an entrepreneurial streak, you’re in luck:  We have an enterprise distribution pipeline, so whatever you build can be deployed straight to enterprise customers with some of the largest code bases in the world, without all the go-to-market hassle you’d encounter in a startup.

You will be a scientist at Sourcegraph Labs doing R&D, and pushing the boundaries of what AI can do, as an IC on our new ML team. You will have the full power of Sourcegraph’s Code Intelligence Platform at your disposal, and you’ll be working on a coding assistant that is already awesome even after just a few weeks of work, so this is a greenfield opportunity to multiply dev productivity to unprecedented levels.

📅 Within one month, you will…

• Start building a trusting relationship with your peers, and learning the company structure.
• Be set up to do local development, and be actively prototyping, and dive deep into how AI and ML is already used at Sourcegraph and identify ways to improve moving forward.
• Start informing the design of ML infra/platform to support deploying of large scale ML models
• Develop simulated datasets using Gym style frameworks across a number of Cody use cases.
• Experiment with changes to Cody prompts, context sources and evaluate the changes with offline experimentation datasets.
• Ship a substantial new feature to end users.

📅 Within three months, you will…

• Be seen as a subject matter expert in all things ML at Sourcegraph.
• Be developing and executing the overall ML infra strategy to maximize impact of ML for Cody users.
• Be ensuring the adoption of best practices in machine learning model development and experimentation.
• Building out feature computation, storage, monitoring, analysis and serving systems for features required across our Cody LLM stack
• Developing distributed training & experiment infrastructure over Code AI datasets, and scaling distributed backe