Machine Learning Engineer, Sourcegraph
$195k
+ Equity
More information about location
3-5 days a week in office
Universal code search and code intelligence for developers
Be an early applicant
Universal code search and code intelligence for developers
101-200 employees
Be an early applicant
$195k
+ Equity
More information about location
3-5 days a week in office
101-200 employees
Company mission
To make it so everyone, in every community, in every country, and in every industry can create products using the best technology.
Role
Who you are
- We are looking for an experienced full stack ML engineer with demonstrated industry experience in productionizing large scale ML models in industrial settings
- 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 Sourcegraph 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 have 5-8 years of industry experience
- You are a backend focused ML engineer who has worked on the entire ML lifecycle
- You have deployed ML models to production to users and have developed feature pipelines
- You understand the nuances of ML for users to move metrics forward
What the job involves
- 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 tightly integrated with our product teams and pushing the boundaries of what AI can do
- You will have the full power of Sourcegraph’s Code Intelligence Platform at your disposal, and you’ll be working on coding agents 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 agents
- 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 agents
- 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
Application process
- We expect the interview process to take 5.5 hours in total
- 👋 Introduction Stage - we have initial conversations to get to know you better…
- [30m] Recruiter Screen
- [45m] Technical Deep Dive
- 🧑💻 Team Interview Stage - we then delve into your experience in more depth and introduce you to members of the team, including cross-functional partners…
- [60m] Resume Deep Dive
- [60m] Architecture Interview
- [15m + async] Pairing Exercise
- 🎉 Final Interview Stage - we move you to our final round, where you gain a better understanding of our business and values holistically…
- [30m] Values
- [30m] Leadership with co-founder
- We check references and conduct your background check
- Please note - you are welcome to request additional conversations with anyone you would like to meet, but didn’t get to meet during the interview process
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, including third-party libraries and any programming language. 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.
Recently the company launched Cody, an open-source AI tool that enhanced coding practices. Utilizing models like GPT-4 Turbo and StarCoder, Cody offers smart code suggestions and contextual insights within popular code editors. As software development continues to evolve, Cody exemplifies Sourcegraph's dedication to driving progress and shaping the future of development with innovative AI solutions.
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 HQ
SoMa, San Francisco, CA
Leadership
Quinn Slack
(CEO)Previously co-founded Blend Labs, an Enterprise Technology company with ~500 employees dedicated to improving home lending. They 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)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. Beyang studied Computer Science at Stanford, where they published research in probabilistic graphical models and computer vision at the Stanford AI Lab.
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