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CodeSignal secures $50M for its tech hiring platform
In less than a year after raising $25 million in Series B funding, technical assessment company CodeSignal announced a $50 million in Series C funding to offer new features for its platform that helps companies make data-driven hiring decisions to find and test engineering talent.
Similar to attracting a big investor lead for its B round — Menlo Ventures — it has partnered with Index Ventures to lead the C round. Menlo participated again and was joined by Headline and A Capital. This round brings CodeSignal’s total fundraising to $87.5 million.
Co-founder and CEO Tigran Sloyan got the idea for the company from an experience his co-founder and friend Aram Shatakhtsyan had while trying to find an engineering job. Both from Armenia, the two went in different paths for college, with Shatakhtsyan staying in Armenia and Sloyan coming to the U.S. to study at MIT. He then went on to work at Google.
“As companies were recruiting myself and my classmates, Aram was trying to get his resume picked up, but wasn’t getting attention because of where he went to college, even though he was the greatest programmer I had ever known,” Sloyan told TechCrunch. “Hiring talent is the No. 1 problem companies say they have, but here was the best engineer, and no one would bring him in.”
They, along with Sophia Baik, started CodeSignal in 2015 to act as a self-driving interview platform that directly measures skills regardless of a person’s background. Like people needing to take a driver’s test in order to get a license, Sloyan calls the company’s technical assessment technology a “flight simulator for developers,” that gives candidates a simulated evaluation of their skills and comes back with a score and highlighted strengths.
The need by companies to hire engineers has led to CodeSignal growing 3.5 times in revenue year over year and to gather a customer list that includes Brex, Databricks, Facebook, Instacart, Robinhood, Upwork and Zoom.
Sloyan said the company has not yet touched the money it received in its Series B, but wanted to jump at the opportunity to work with Nina Achadjian, partner at Index Ventures, whom he had known for many years since their time together at Google. To work together and for Achadjian to join the company’s board was something “I couldn’t pass up,” Sloyan said.
When Achadjian moved over to venture capital, she helped Sloyan connect to mentors and angel investors while keeping an eye on the company. Hiring engineers is “mission critical” for technology companies, but what became more obvious to her was that engineering functions have become necessary for all companies, Achadjian explained.
While performing due diligence on the space, she saw traditional engineering cultures utilizing CodeSignal, but then would also see nontraditional companies like banks and insurance companies.
“Their traction was undeniable, and many of our portfolio companies were using CodeSignal,” she added. “It is rare to see a company accelerate growth at the stage they are at.”
U.S. Department of Labor statistics estimate there is already a global talent labor shortage of 40 million workers, and that number will grow to over 85 million by 2030. Achadjian says engineering jobs are also expected to increase during that time, and with all of those roles and applicants, vetting candidates will be more important than ever, as will the ability for candidates to apply from wherever they are.
The new funding enabled the company to launch its Integrated Development Environment for candidates to interact with relevant assessment experiences like codes, files and a terminal on a machine that is familiar with them, so that they can showcase their skills, while also being able to preview their application. At the same time, employers are able to assign each candidate the same coding task based on the open position.
In addition, Sloyan intends to triple the company’s headcount over the next couple of months and expand into other use cases for skills assessment.
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Liquid Instruments raises $13.7M to bring its education-focused 8-in-1 engineering gadget to market
Part of learning to be an engineer is understanding the tools you’ll have to work with — voltmeters, spectrum analyzers, things like that. But why use two, or eight for that matter, where one will do? The Moku:Go combines several commonly used tools into one compact package, saving room on your workbench or classroom while also providing a modern, software-configurable interface. Creator Liquid Instruments has just raised $13.7 million to bring this gadget to students and engineers everywhere.
Image Credits: Liquid Instruments
The idea behind Moku:Go is largely the same as the company’s previous product, the Moku:Lab. Using a standard input port, a set of FPGA-based tools perform the same kind of breakdowns and analyses of electrical signals as you would get in a larger or analog device. But being digital saves a lot of space that would normally go toward bulky analog components.
The Go takes this miniaturization further than the Lab, doing many of the same tasks at half the weight and with a few useful extra features. It’s intended for use in education or smaller engineering shops where space is at a premium. Combining eight tools into one is a major coup when your bench is also your desk and your file cabinet.
Those eight tools, by the way, are: waveform generator, arbitrary waveform generator, frequency response analyzer, logic analyzer/pattern generator, oscilloscope/voltmeter, PID controller, spectrum analyzer and data logger. It’s hard to say whether that really adds up to more or less than eight, but it’s definitely a lot to have in a package the size of a hardback book.
You access and configure them using a software interface rather than a bunch of knobs and dials — though let’s be clear, there are good arguments for both. When you’re teaching a bunch of young digital natives, however, a clean point-and-click interface is probably a plus. The UI is actually very attractive; you can see several examples by clicking the instruments on this page, but here’s an example of the waveform generator:
Image Credits: Liquid Instruments
Love those pastels.
The Moku:Go currently works with Macs and Windows but doesn’t have a mobile app yet. It integrates with Python, MATLAB and LabVIEW. Data goes over Wi-Fi.
Compared with the Moku:Lab, it has a few perks. A USB-C port instead of a mini, a magnetic power port, a 16-channel digital I/O, optional power supply of up to four channels and of course it’s half the size and weight. It compromises on a few things — no SD card slot and less bandwidth for its outputs, but if you need the range and precision of the more expensive tool, you probably need a lot of other stuff too.
Image Credits: Liquid Instruments
Since the smaller option also costs $500 to start (“a price comparable to a textbook”… yikes) compared with the big one’s $3,500, there’s major savings involved. And it’s definitely cheaper than buying all those instruments individually.
The Moku:Go is “targeted squarely at university education,” said Liquid Instruments VP of marketing Doug Phillips. “Professors are able to employ the device in the classroom and individuals, such as students and electronic engineering hobbyists, can experiment with it on their own time. Since its launch in March, the most common customer profile has been students purchasing the device at the direction of their university.”
About a hundred professors have signed on to use the device as part of their fall classes, and the company is working with other partners in universities around the world. “There is a real demand for portable, flexible systems that can handle the breadth of four years of curriculum,” Phillips said.
Production starts in June (samples are out to testers), the rigors and costs of which likely prompted the recent round of funding. The $13.7 million comes from existing investors Anzu Partners and ANU Connect Ventures, and new investors F1 Solutions and Moelis Australia’s Growth Capital Fund. It’s a convertible note “in advance of an anticipated Series B round in 2022,” Phillips said. It’s a larger amount than they intended to raise at first, and the note nature of the round is also not standard, but given the difficulties faced by hardware companies over the last year, some irregularities are probably to be expected.
No doubt the expected B round will depend considerably on the success of the Moku:Go’s launch and adoption. But this promising product looks as if it might be a commonplace item in thousands of classrooms a couple years from now.
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$4 million richer, Walrus.ai has a pitch for companies looking for QA-testing tools
The co-founders of Walrus.ai, a new software company that raised $4 million in a new round of financing from Homebrew, Felicis Ventures and Leadout Capital, started their business with one problem.
Jake Marsh, Ogden Nathan and Scott White had a problem. They left Wealthfront to launch a new service that would solve what they saw as a key problem with new business workflows. Their idea was to integrate the disparate software silos that different parts of their former business used to complete assignments.
The company was going to be called Monolist and it was going to aggregate tasks across every tool into a single actionable list. Unfortunately it wasn’t working.
They had founded the business back in 2018 and had gone on to raise seed capital from Homebrew and Leadout Capital, but they were hitting walls in their product development.
“Reliability was a huge problem for us,” said company co-founder, Scott White. “There were various frameworks that would let you test your automation so that before you launch your software, you catch bugs… There were some code languages that exist that can help you do this, but they didn’t work for us at all.”
The browser testing frameworks that White and his co-founders were using hadn’t kept up with the evolution of the software development industry and couldn’t adequately recreate the ways that actual users would interact with the software. “The stuff is super brittle,” said White.
Typically, according to White, these assurance tests break and then force engineers and developers to then investigate why the tests broke, to see if they can figure out what went wrong with the test even before they move on to any quality assurance of the actual changes made to a product.
“They weren’t designed to handle that much complexity,” White said of the existing testing tools.
So White and his co-founders thought about how they’d solve what they see as one of the critical problems that engineers face.
“The problem for engineers right now is that writing tests for your applications is hard because you have to write code and the frameworks are very inflexible and flaky,” White said. “Engineers spend tons of time running tests and if those tests fail then your code would not get shipped so you have to debut all those tests.”
Enter the new venture from White and his co-founders.
That would be Walrus.ai . “We’re outsourced engineering through an API,” said White. “We understand how to do testing and we can do it way better and more quickly.”
Using simple text descriptions of a planned user interface, Walrus.ai’s co-founder said his company can run diagnostics on just how effectively the code manages to execute its planned commands.
Given its status as a relatively new kind on the testing block, Walrus.ai only has tens of paying customers right now as it spins out from Monolist.
The company sees its competition coming primarily from outsourced quality assurance companies like Rainforest QA; test recorders like Mabel and Testim; and testing frameworks like Selenium and Cypress, but believes that its ability to take natural language prompts and run QA tests will be enough of a differentiator to capture a significant share of the market.
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Pinpoint releases dashboard to bring visibility to software engineering operations
As companies look for better ways to understand how different departments work at a granular level, engineering has traditionally been a black box of siloed data. Pinpoint, an Austin-based startup, has been working on a platform to bring this information into a single view, and today it released a dashboard to help companies understand what’s happening across software engineering from an operational perspective.
Jeff Haynie, co-founder and CEO at Pinpoint says the company’s mission for the last two years has been giving greater visibility into the engineering department, something he says is even more important in the current context with workers spread out at home.
“Companies give engineering a bunch of money, and they build a bunch of amazing things, but in the end, it is just a black box, and we really don’t know what happens,” Haynie said. He says his company has been working to take all of the data to try and contextualize it, bring it together and correlate that information.
Today, they are introducing a dashboard that takes what they’ve been building and pulls it together into a single view, which is 100% self-serve. Prior to this, you needed a bunch of hand-holding from Pinpoint personnel to get it up and running, but today you can download the product and sign into your various services such as your git repository, your CI/CD software, your IDE and so forth.
It also provides a way for engineering personnel to communicate with one another without leaving the tool.
Pinpoint software engineering dashboard. Image Credit: Pinpoint
“Obviously, we will handhold and help people as they need it, and we have an enterprise version of the product with a higher level of SLA, and we have a customer success team to do that, but we’ve really focused this new release on purely self service,” Haynie said.
What’s more, while there is a free version already for teams under 10 people that’s free forever, with the release of today’s product, the company is offering unlimited access to the dashboard for free for three months.
Haynie says they’re like any startup right now, but having experience with several other startups and having lived through 9/11, the dot-com crash, 2008 and so forth, he knows how to hunker down and preserve cash. At the same time, he says they are seeing a lot of in-bound interest in the product, and they wanted to come up with a creative way to help customers through this crisis, while putting the product out there for people to use.
“We’re like any other startup or any other business frankly at this point: we’re nervous and scared. How do you survive this [and how long will it last]? The other side of it is that we’re rushing to take advantage of this inbound interest that we’re getting and trying to sort of seize the opportunity and try to be creative about how we help them.”
The startup hopes that, if companies find the product useful, after three months they won’t mind paying for the full version. For now, it’s just putting it out there for free and seeing what happens with it — just another startup trying to find a way through this crisis.
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How Roblox avoided the gaming graveyard and grew into a $2.5B company
There are successful companies that grow fast and garner tons of press. Then there’s Roblox, a company which took at least a decade to hit its stride and has, relative to its current level of success, barely gotten any recognition or attention.
Why has Roblox’s story gone mostly untold? One reason is that it emerged from a whole generation of gaming portals and platforms. Some, like King.com, got lucky or pivoted their business. Others by and large failed.
Once companies like Facebook, Apple and Google got to the gaming scene, it just looked like a bad idea to try to build your own platform — and thus not worth talking about. Added to that, founder and CEO Dave Baszucki seems uninterested in press.
But overall, the problem has been that Roblox just seemed like an insignificant story for many, many years. The company had millions of users, sure. So did any number of popular games. In its early days, Roblox even looked like Minecraft, a game that was released long after Roblox went live, but that grew much, much faster.
Yet here we are today: Roblox now claims that half of all American children aged 9-12 are on its platform. It has jumped to 90 million monthly unique users and is poised to go international, potentially multiplying that number. And it’s unique. Essentially all other distribution services offering games through a portal have eventually fizzled, aside from some distant cousins like Steam.
This is the story of how Roblox not only survived, but built a thriving platform.
Seeds of an idea
(Photo by Steve Jennings/Getty Images for TechCrunch)
Before Roblox, there was Knowledge Revolution, a company that made teaching software. While designed to allow students to simulate physics experiments, perhaps predictably, they also treated it like a game.
“The fun seemed to be in building your own experiment,” says Baszucki. “When people were playing it and we went into schools and labs, they were all making car crashes and buildings fall down, making really funny stuff.” Provided with a sandbox, kids didn’t just make dry experiments about mass or velocity — they made games, or experiences they could show off to friends for a laugh.
Knowledge Revolution was founded in 1989, by Dave Baszucki and his brother Greg (who didn’t later co-found Roblox, but is now on its board). Nearly a decade later, it was acquired for $20 million by MSC Software, which made professional simulation tools. Dave continued there for another four years before leaving to become an angel investor.
Baszucki put money into Friendster, a company that pre-dated Facebook and MySpace in the social networking category. That investment seeded another piece of the idea for Roblox. Taken together, the legacy of Knowledge Revolution and Friendster were the two key components undergirding Roblox: a physics sandbox with strong creation tools, and a social graph.
Baszucki himself is a third piece of the puzzle. Part of an older set of entrepreneurs, which might be called the Steve Jobs generation, Baszucki’s archetype seems closer to Mr. Rogers than Jobs himself: unfailingly polite and enthusiastic, never claiming superior insight, and preferring to pass credit for his accomplishments on to others. In conversation, he shows interests both central and tangential to Roblox, like virtual environments, games, education, digital identity and the future of tech. Somewhere in this heady mix, the idea of Roblox came about.
The first release
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Tara.ai, which uses machine learning to spec out and manage engineering projects, nabs $10M
Artificial intelligence has become an increasingly important component of how a lot of technology works; now it’s also being applied to how technologists themselves work. Today, one of the startups building such a tool has raised some capital, Tara.ai, a platform that uses machine learning to help an organization get engineering projects done — from identifying and predicting the work that will need to be tackled, to sourcing talent to execute that, and then monitoring the project of that project — has raised a Series A of $10 million to continue building out its platform.
The funding for the company cofounded by Iba Masood (she is the CEO) and Syed Ahmed comes from an interesting group of investors that point to Tara’s origins, as well as how it sees its product developing over time.
The round was led by Aspect Ventures (the female-led firm that puts a notable but not exclusive emphasis on female-founded startups) with participation also from Slack, by way of its Slack Fund. Previous investors Y Combinator and Moment Ventures also participated in the round. (Y Combinator provides an avenue to companies from its cohorts to help them source their Series A rounds, and Tara.ai went through this process.)
Tara.ai was originally founded as Gradberry out of Y Combinator, with its initial focus on using an AI platform for organizations to evaluate and help source engineering talent: Tara.ai was originally that name of its AI engine.
(The origin of how Masood and Ahmed identified this problem was through their own direct experience: both were grads (she in finance, he in engineering) from the American University of Sharjah in the U.A.E. that had problems getting hired because no one had ever heard of their university. Even so, they had won an MIT-affiliated startup competition in Morocco and relocated to Boston. The idea with Gradberry was to cut through the big names and focus just on what people could do.)
Masood and Syed (who eventually got married) eventually realised that using that engine to evaluate the wider challenges of executing engineering projects came as a natural progression once the team started digging into the challenges and identifying what actually needed to be solved.
A study that McKinsey (where Masood once worked) conducted across some 5,000 projects found that $66 billion dollars were identified as “lost” due to projects running past the expected completion time, lack of adequate talent and just overall poor planning.
“We realised that recruiting was actually the final decision you make, not the first, and we wanted to be involved earlier in the decision-making process,” Masood said in an interview. “We saw a much bigger opportunity looking not at the people, but the whole project.”
In action, that means that Tara.ai is used not just to scope out the nature of the problem that needed to be solved, or the goal that an organization wanted to achieve; it is also used to suggest which frameworks will need to be used to execute on that goal, and then suggest a timeline to follow.
Then, it starts to evaluate a company’s own staff expertise, along with that from other recruiting platforms, to figure out which people to source from within the company. Eventually, that will also be complemented with sourcing information from outside the organization — either contractors or new hires.
Masood noted that a large proportion of users in the tech world today use Jira and platforms like it to manage projects. While there are some tools in Jira to help plan out projects better, Tara is proposing its platform as a kind of virtual project manager, or an assistant to an existing project manager, to conceive of the whole project, not just help with the admin of getting it done.
Notably, right now she says that some 75% of Tara.ai’s users — customers include Cisco, Orange Silicon Valley and Mower Digital — are “not technical,” meaning they themselves do not ship or use code. “This helps them understand what could be considered and the dependencies that can be expected out of a project,” she notes.
Lauren Kolodny, the partner at Aspect who led the investment, said that one of the things that stood out for her, in fact, with Tara.ai, was precisely how it could be applied exactly in those kinds of scenarios.
Today, tech is such a fundamental part of how a lot of businesses operate, but that doesn’t mean that every business is natively a technology one (think here of food and beverage companies as an example, or government agencies). In those cases, these companies would have traditionally had to turn to outside consultants to identify opportunities, and then build and potentially long-term operate whatever the solutions become. Now there is an opportunity to rethink how technology is used in these kinds of organizations.
“Projects have been hacked together from multiple systems, not really built in combination,” Kolodny said of how much development happens at these traditional businesses. “We are really excited about the machine learning scoping and mapping of internal and external talent, which is looking to be particularly important as traditional enterprises are required to get level with newer businesses, and the amount of talent they need to execute on these projects becomes challenging.”
Tara.ai’s next steps will involve essentially taking the building blocks of what you can think of as a very powerful talent and engineering project search engine, and making it more powerful. That will include integrating databases of external consultants and figuring out how best to have these in tandem with internal teams while keeping them working well together. And soon to come also will be bug prediction: how to identify these before they arise in a project. The company is releasing an updated AI engine to coincide with the funding.
The Slack investment is also a notable nod to what direction Tara.ai will take. Masood said that Slack was one of three “big tech” companies interested in investing in this round, and she and Syed chose Slack because from what they could see of its existing and target customers, many were already using it and some have already started requesting closer collaboration so that events in one could come up as updates in the other.
“Our largest customers are heavy Slack users and they are already having conversations in Slack related to projects in Tara.ai,” she said. “We are tackling the scoping element and now seeing how to link up even command line interfaces between the two.”
She noted that this does not rule out closer integrations with communications and other platforms that people use on a daily basis to get their work done: the idea is to become a tool to work better overall.
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Three years after moving off AWS, Dropbox infrastructure continues to evolve
Conventional wisdom would suggest that you close your data centers and move to the cloud, not the other way around, but in 2016 Dropbox undertook the opposite journey. It (mostly) ended its long-time relationship with AWS and built its own data centers.
Of course, that same conventional wisdom would say, it’s going to get prohibitively expensive and more complicated to keep this up. But Dropbox still believes it made the right decision and has found innovative ways to keep costs down.
Akhil Gupta, VP of Engineering at Dropbox, says that when Dropbox decided to build its own data centers, it realized that as a massive file storage service, it needed control over certain aspects of the underlying hardware that was difficult for AWS to provide, especially in 2016 when Dropbox began making the transition.
“Public cloud by design is trying to work with multiple workloads, customers and use cases and it has to optimize for the lowest common denominator. When you have the scale of Dropbox, it was entirely possible to do what we did,” Gupta explained.
Alone again, naturally
One of the key challenges of trying to manage your own data centers, or build a private cloud where you still act like a cloud company in a private context, is that it’s difficult to innovate and scale the way the public cloud companies do, especially AWS. Dropbox looked at the landscape and decided it would be better off doing just that, and Gupta says even with a small team — the original team was just 30 people — it’s been able to keep innovating.
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Databricks open-sources Delta Lake to make data lakes more reliable
Databricks, the company founded by the original developers of the Apache Spark big data analytics engine, today announced that it has open-sourced Delta Lake, a storage layer that makes it easier to ensure data integrity as new data flows into an enterprise’s data lake by bringing ACID transactions to these vast data repositories.
Delta Lake, which has long been a proprietary part of Databrick’s offering, is already in production use by companies like Viacom, Edmunds, Riot Games and McGraw Hill.
The tool provides the ability to enforce specific schemas (which can be changed as necessary), to create snapshots and to ingest streaming data or backfill the lake as a batch job. Delta Lake also uses the Spark engine to handle the metadata of the data lake (which by itself is often a big data problem). Over time, Databricks also plans to add an audit trail, among other things.
“Today nearly every company has a data lake they are trying to gain insights from, but data lakes have proven to lack data reliability. Delta Lake has eliminated these challenges for hundreds of enterprises. By making Delta Lake open source, developers will be able to easily build reliable data lakes and turn them into ‘Delta Lakes’,” said Ali Ghodsi, co-founder and CEO at Databricks.
What’s important to note here is that Delta lake runs on top of existing data lakes and is compatible with the Apache spark APIs.
The company is still looking at how the project will be governed in the future. “We are still exploring different models of open source project governance, but the GitHub model is well understood and presents a good trade-off between the ability to accept contributions and governance overhead,” Ghodsi said. “One thing we know for sure is we want to foster a vibrant community, as we see this as a critical piece of technology for increasing data reliability on data lakes. This is why we chose to go with a permissive open source license model: Apache License v2, same license that Apache Spark uses.”
To invite this community, Databricks plans to take outside contributions, just like the Spark project.
“We want Delta Lake technology to be used everywhere on-prem and in the cloud by small and large enterprises,” said Ghodsi. “This approach is the fastest way to build something that can become a standard by having the community provide direction and contribute to the development efforts.” That’s also why the company decided against a Commons Clause licenses that some open-source companies now use to prevent others (and especially large clouds) from using their open source tools in their own commercial SaaS offerings. “We believe the Commons Clause license is restrictive and will discourage adoption. Our primary goal with Delta Lake is to drive adoption on-prem as well as in the cloud.”
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Facebook is using machine learning to self-tune its myriad services
Regardless of what you may think of Facebook as a platform, they run a massive operation, and when you reach their level of scale you have to get more creative in how you handle every aspect of your computing environment.
Engineers quickly reach the limits of human ability to track information, to the point that checking logs and analytics becomes impractical and unwieldy on a system running thousands of services. This is a perfect scenario to implement machine learning, and that is precisely what Facebook has done.
The company published a blog post today about a self-tuning system they have dubbed Spiral. This is pretty nifty, and what it does is essentially flip the idea of system tuning on its head. Instead of looking at some data and coding what you want the system to do, you teach the system the right way to do it and it does it for you, using the massive stream of data to continually teach the machine learning models how to push the systems to be ever better.
In the blog post, the Spiral team described it this way: “Instead of looking at charts and logs produced by the system to verify correct and efficient operation, engineers now express what it means for a system to operate correctly and efficiently in code. Today, rather than specify how to compute correct responses to requests, our engineers encode the means of providing feedback to a self-tuning system.”
They say that coding in this way is akin to declarative code, like using SQL statements to tell the database what you want it to do with the data, but the act of applying that concept to systems is not a simple matter.
“Spiral uses machine learning to create data-driven and reactive heuristics for resource-constrained real-time services. The system allows for much faster development and hands-free maintenance of those services, compared with the hand-coded alternative,” the Spiral team wrote in the blog post.
If you consider the sheer number of services running on Facebook, and the number of users trying to interact with those services at any given time, it required sophisticated automation, and that is what Spiral is providing.
The system takes the log data and processes it through Spiral, which is connected with just a few lines of code. It then sends commands back to the server based on the declarative coding statements written by the team. To ensure those commands are always being fine-tuned, at the same time, the data gets sent from the server to a model for further adjustment in a lovely virtuous cycle. This process can be applied locally or globally.
The tool was developed by the team operating in Boston, and is only available internally inside Facebook. It took lots of engineering to make it happen, the kind of scope that only Facebook could apply to a problem like this (mostly because Facebook is one of the few companies that would actually have a problem like this).
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Materialize.X is using machine learning to disrupt the $300B engineered wood industry
What’s the next $300 billion industry to be disrupted by technology? Wood. Specifically, engineered wood. Materialize.X, launching today at TechCrunch Disrupt SF 2017, has two new products that it thinks will revolutionize the $300 billion-a-year engineered wood market. Read More
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