Machine Learning Engineer, Sourcegraph
$185k
Plus equity
More information about location
Universal code search for developers
Job no longer available
Universal code search for developers
101-200 employees
Job no longer available
$185k
Plus equity
More information about location
101-200 employees
Company mission
To make it so everyone codes by building the tools that keep developers at the bleeding edge.
Role
Who you are
- 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
- If you’ve been anywhere near the field lately, you can probably pick up enough about LLM capabilities to be able to drive this space, as it’s all greenfield
- 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
What the job involves
- 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
- 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
Application process
- Interview process [~5.5 hour total interview]
- 👋 Introduction Stage - we have initial conversations to get to know you better…
- [30m] Recruiter Screen with Devon Coords
- [60m] Hiring Manager Screen / ML Depth with Rishabh Mehrotra
- 🧑💻 Team Interview Stage - we then delve into your experience in more depth and introduce you to members of the team…
- [45m] Technical Deep Dive with Dominic Cooney + Julie Tibshirani
- [60m] Architecture Interview with Rok Novosel
- [Async] Pairing Exercise with Beyang Liu
- 🎉 Final Interview Stage - we move you to our final round, where you will gain a better understanding of our business holistically…
- [30m] Values Interview
- [30m] Leadership Interview with Steve Yegge
- [30m] Leadership Interview with Quinn Slack
- We check references and conduct your background check
Our take
Inspired by code search engines like OpenGrok, Livegrep and Google Code Search, Sourcegraph has developed a robust tool that allows developers to swiftly search through code across an entire organization. While similar systems are used by large-scale organizations, Sourcegraph aims to make this powerful code search capability accessible to every developer.
Searching through extensive codebases, especially during debugging, can be time-consuming and tiresome. Sourcegraph streamlines this process, saving time and reducing stress for both individuals and businesses. The platform offers a free version for all developers, while revenue is generated through enterprise-specific features tailored for large corporations.
The launch of Cody, an open-source AI tool that enhances coding practices, saw the company expand into AI coding agents - although this part of its business is now splitting off into "Amp". This leaves the Sourcegraph team to focus on delivering and innovating its code search product, which is now a mission-critical piece of AI infrastructure software for many businesses.
Steph
Company Specialist at Welcome to the Jungle
Insights
-20% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Jul 2021
$125m
SERIES D
Dec 2020
$50m
SERIES C
Total funding: $223m
Company benefits
- Medical, dental and vision (100% company-paid)
- Unlimited PTO with a 30 day minimum
- Wellness stipend
- Generous travel budgets
- Family planning benefits
- Equity
- Office budget
- Professional development
- Global, remote-friendly team with hubs in key cities
Company HQ
SoMa, San Francisco, CA
Leadership
Quinn Slack
(Board Member & Co-founder)CEO and Member of Technical Staff at Amp Code. Previously co-founded Blend Labs, and also worked at Palantir Technologies where they created a technology platform to help two of the top five U.S. banks recover from the housing crisis.
Beyang Liu
(CTO & Co-founder)Previously a Software Engineer at Palantir Technologies, where they developed new data analysis software on a small, customer-facing team working with Fortune 500 companies. Studied Computer Science at Stanford.
Dan Adler
(CEO)Joined the company in 2016 as Director of BizOps and Strategy. Became VP of Business in 2018, then CEO in 2025. Previously founded Cells Software, was an Investment Associate at Vector Capital, and Consultant at Bain & Company.
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