Machine Learning Engineer

Responsibilities

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 couple of major ML components, including their modeling and ML infra roadmap.

About you 

You are an experienced full stack ML engineer with demonstrated industry experience in formulating ML solutions, developing end to end data orchestration pipelines, deploying large scale ML models and experimenting offline and online to drive business impact for Cody users. You want to be part of a world-class team to push the boundaries of AI, with a particular focus on leveraging Sourcegraph’s code intelligence to leapfrog competitors.

First, your AI background could look like a few different things:

• You’ve worked on AI systems and have built ML at large tech companies, specifically experience in developing and productionising machine learning models.
• Hands-on experience using data processing tools like Beam, Spark or Flink in a cloud environment like GCP or AWS and first-hand knowledge about data management concepts.
• You have a deep ML background and have demonstrated an ability to be customer and company focused. You are hands-on and can build machine learning
• Hands-on experience training and serving large-scale (10GB+) models using frameworks such as Tensorflow or PyTorch
• Experience with Docker, Kubernetes, Kubeflow or Flink, knowledge of CI/CD in the context of ML pipelines
• You have some hands-on experience working with large foundational models and their toolkits. Familiarity with LLMs such as Llama, StarCoder etc., model fine-tuning techniques (LORA, QLORA), prompting techniques (Chain of Thought, ReACT, etc) and model evaluation.
• You’ve worked in NLP or language models at a top-tier research lab