wants to help recruiters find technical talent by inferring skills from GitHub code

Companies already have a plethora of tools at their disposal for finding technical talent, but a startup wants to help recruiters by bringing together the worlds of GitHub and LinkedIn to create a database of the most suitable candidates for a development. of specific software. paper, and it’s doing it by using AI to “infer” abilities from the code they’ve written., as the company is called, allows recruiters to search for developers based on their technical skills, the libraries they’ve used, or simply the contributions they’ve made to projects on GitHub.

Founded in San Francisco in 2022, is the brainchild of CEO Maria Grineva, who sold a previous data startup called Orb Intelligence to Dun & Bradstreet in 2020; CTO Fedor Soprunov, formerly a machine learning researcher at Russian tech titan Yandex; and product manager Dmitry Pyanov, who has worked on product teams at companies like Yandex and Replika.

While recruiting is the company’s primary focus initially, opening its inaugural recruiter product in closed beta this week, Grineva sees a wide range of use cases beyond helping companies fill technical roles. This includes fostering relationships with developers, such as asking them to join a community or inviting them to contribute to an open source project; solicit their expertise for a specific problem; and even to help developer tool companies introduce their products.

“This week we will launch for tech recruiters, and in April we will expand our SaaS offering with for developer relations to help companies building tools for developers to understand their TAM (total addressable market), learn more about your existing developer community and reach your target audience,” Grineva explained to TechCrunch.

To help fuel its business momentum, announced today that it has raised $1 million in pre-seed funding from Germany-based angel fund Angel Invest, Brooklyn Bridge Ventures, and a host of angel backers, including one of the first employees of Spotify and its former CTO, Andreas Ehn.

analyze that

So how does infer abilities from public source code? Well, first of all, the platform executes the GitHub “git clone” command, which creates a copy of millions of public repositories and branches. then parses each git commit and inspects the snippet, file path, and subject of the commit to find out what it is.

“For a given project, we can see who is the lead architect, who develops the back-end or front-end, who focuses on UI/UX, who builds QA and testing, and who are the writers. technicians,” Grineva said. also takes a close look at git actions such as pull requests, including rejections and approvals, comments, and issue opens, which is to help “understand” the different roles and levels. commitment of project contributors.

“We process not only famous open source projects, but also ‘pet’ projects, tests, forks, and even training projects from Coursera or Udemy that engineers keep public on GitHub,” added Grineva. “In total, we’re processing about a billion GitHub commits per year to get a very accurate profile of each engineer’s skills.”

Under the hood, relies on OpenAI’s GPT, adapting the much-hyped language model in high-profile open source projects and StackOverflow articles to help you score on code quality, for example. profile example. Image Credits: users can create lists of top experts in specific disciplines, such as “big language models” or “computer vision”, and generate a leaderboard of the best in a given field. Or they can submit a list of repositories and create a ranking of all contributors by the number of commits they’ve made.

Effectively, recruiters and companies can tailor their search to whatever parameters they like, including skill areas, programming languages, and number of years of experience. search example. Image Credits:

But understanding code is only part of what offers.

A key selling point for recruiters is the ability to connect with software developers, and for that includes an integrated email outreach engine, powered by the sales engagement platform.

“Users use our search to build a list of relevant candidates, and can then create a personalized email sequence, mentioning candidates by name, referring to their projects, and explaining why they think a job role is a good fit for them. them,” Grineva said. . – Email broadcast example. Image Credits:

Recruiters will also probably want a more comprehensive look at a developer’s skills, education, and employment history, which they probably won’t get from GitHub. This is where LinkedIn enters the fray, with collecting publicly available data and aligning it with the corresponding GitHub individual. And this is what Grineva says is the special sauce of the platform: By combining data from two widely used platforms, you can create a more detailed picture of potential candidates.

“I think joining GitHub and LinkedIn profiles brings a lot of value, as engineers are generally not very good at promoting themselves and often don’t even have full LinkedIn profiles,” Grineva said. “Also, on LinkedIn, people describe themselves, which means the information is subjective. Applying a standard methodology to infer the skills of all engineers based on their actual contributions to the code not only eliminates subjectivity, but also means that companies will be able to evaluate candidates uniformly.”


Of course, none of this offers a perfect recruiting pipeline. Putting two disparate, gigantic data sets together is no easy task, and there is likely a lot of room for error here, with similar names and histories increasing the potential for profiling. And that’s assuming a person has a LinkedIn profile in the first place, which he may not. But under the hood, Grineva said they have implemented measures that go some way to addressing at least some of those potential pitfalls.

“Combining two large data sets is not an easy task, as the information that people make available on GitHub can be sparse, and many engineers choose to remain anonymous on GitHub,” explained Grineva. “We’ve created a proprietary fuzzy-match system that takes into account not only names, usernames, and email addresses, but also workplaces, experience, and interests.”

On top of that, Grineva said that they use computer vision to compare profile avatars across platforms, which while not foolproof on its own, serves as an additional tool alongside their other verification mechanisms.

At the time of writing, claims to have the contact information of around 70% of all the profiles in its database, which obviously means that 30% lack that crucial information. At that point, Grineva said that while they hope to improve their contact detail coverage as it expands, their potential use cases won’t always revolve around communication.

“Another important use case is data enrichment,” he said. “Customers can search for the candidate’s full profile by GitHub ID, LinkedIn URL, or contact email, in which case we can only match those 70% where we have email.”

There’s also the giant elephant in the room here: Isn’t just facilitating “cold calling” seeking to contact developers en masse?

“There is a risk, but it’s important to first recognize that recruiters are already trying to cold call developers and this is currently happening through other tools, as well as some tech recruiters manually pulling contact information directly from GitHub.” Grineva said. “That being said, recruiters are currently doing this with poor or limited information about the developers they are approaching, which means the outreach is not personalized and often the opportunity is not a good fit for developers. As a result, these emails are considered spam.”

For those on the receiving end of a outreach campaign, Grineva noted that the platform is “fully GDPR compliant” and developers can ask you to delete or edit their profiles, as well as opt out. broadcast by email.

Show me the money

It’s still early days for and it’s experimenting with different plans, but the company is essentially operating a SaaS-based subscription model, with pricing based on the number of contacts a user accesses. This starts at “free” for up to 100 contacts per month, up to a “recruiter” plan, which costs $530 per month for advanced search features and 3,000 contacts. It also offers a business plan with custom pricing, which is available upon request.

You also can’t ignore the myriad of other recruiting solutions out there, spanning everything from LinkedIn’s own Talent Solutions product, to Zoominfo, SeekOut, TalentOS, and HireEZ. But Grineva says that’s focus purely on technical talent and its GitHub-scanning savvy is what sets it apart from the crowd. In turn, this could they mean more focused headhunting efforts, where a recruiter’s and candidate’s goals are more closely aligned.

“Being an engineer myself, I get a lot of messages from recruiters that are not relevant to me and I see this problem firsthand,” Grineva said. “I think this is mostly a data quality issue – recruiters just don’t have enough information about me to match me to interesting opportunities. Our goal is to reduce the level of noise that developers receive today. By giving recruiters better information, we believe this will be beneficial to both developers and recruiters.”

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James D. Brown
James D. Brown
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