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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer [IC4] - **Company:** Sourcegraph, Inc. - **Location:** San Francisco, CA, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Static Program Analysis, Compilers, Continuous Integration, Machine Learning, Language Modeling, Tensorflow, Type Systems, Virtual Machines, QLoRA, Data Processing, Pytorch, ReactJS, Large Language Models, Prompt Engineering, Apache Spark, Model Validation, Backend, Core Data, Kubernetes, Low Latency, Apache Flink, Data Management, Machine Learning Operations, Docker, Programming Languages - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25303_8c48cc3c8d437e6bcd52344b88bcfa85 ## About the Role 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 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., * 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 Second, you have some understanding of programming languages, and tools that manipulate code. This could have taken any number of forms; e.g.: * You've worked with grammars and parser generators, or Treesitter * You've worked with compilers and semantic analysis, e.g. type systems * You've written an interpreter, or worked on a virtual machine * You've done static analysis involving scanning source code for semantic information It doesn't really matter how you know it, but it's important that you're familiar with the basic concepts of semantic representations of source code, and how they're produced and consumed by tooling. ## Description * 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.