Full-Time ML Engineer IC3

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 an engineer at Sourcegraph doing R&D, and pushing the boundaries of what AI can do, as an IC on our 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 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.
• Dive deep into how AI and ML is already used at Sourcegraph and identify ways to improve moving forward.
• 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'¦

• Building out feature computation, storage, monitoring, analysis and serving systems for features required across our Cody LLM stack
• Be contributing actively to the world'™s best coding assistant.
• Developing distributed training & experiment infrastructure over Code AI datasets, and scaling distributed backend services to reliably support high-QPS low latency use cases.
• Be following all the relevant research, and conducting research of your own.

📅 Within six months, you will'¦

• Be fully ramped up and owning key pieces of the assistant.
• Be ramped up on other relevant parts of the Sourcegraph product.
• Be helping design and build what might become the biggest dev accelerator in 20 years.
• Owning a number of ML systems, and building core data and model metadata systems powering the end-to-end ML lifecycle.
• Be developing a highly scalable, high-QPS inference service providing low latency performance using a mix of CPU and GPU hardware to most efficiently utilize resources.
• Be driving the technical vision and owning a couple of major ML components, including their modeling and ML infra roadmap.