The Ladder of Opportunities

“If you give a man a fish, you feed him for a day—if you teach him to fish, you feed him for many days.”

This is a Chinese proverb that has been proven true to people even during modern times. Teaching people how to do something is better than doing it for them.

But a computer science genius has found this principle more beneficially and profoundly. 

The Ladder of Opportunities

Founder and CEO of Turing, Jonathan Siddharth, helps companies build engineering teams through a cloud-based data science platform.


Jonathan Siddharth has a Bachelor’s Degree in Computer Science from Anna University. Jonathan graduated 1st rank in his class in the SVCE’s Computer Science Department. In his sophomore year, Jonathan Siddharth published his first peer-reviewed IEEE paper on Artificial Neural Networks for Self-Driving Cars. Siddharth’s work was presented at the IEEE Conference on AI in Singapore.

During his undergraduate studies, Jonathan Siddharth received awards in a few semesters. Lucas TVS Merit Award, Merit Awards for 1st Rank in Computer Science, and CAT Prize 1st Rank in Continuous Assessment tests.


After finishing his undergraduate studies, Jonathan Siddharth proceeded to Stanford. He became a graduate student in Computer Science, working with Prof. Hector Garcia-Molina and Dr. Andreas Paepcke of the Stanford InfoLab. Jonathan completed his graduate studies recognized in Masters with Distinction in Research and was awarded the Christopher Stephenson Memorial Award for Best Masters Research in the Computer Science Department.

Siddharth’s work on SpotSigs, later published in SIGIR, also won Stanford's Best Masters Thesis Award.


Currently, he serves as Co-Founder & CEO of Flipora, a mood-based web discovery service with 30 million users he co-founded with Vijay Krishnan while still a Stanford student.

Early Career

In 2006, Siddharth started working as an intern in Yahoo! Machine Learned Ranking.

Yahoo! Machine Learned Ranking

While working at Yahoo!'s Machine Learned Search Ranking group, Jonathan Siddharth worked on Automatic Search Regionalization, which resulted in one of Yahoo!'s most extensive results that year for a single feature.

There is the problem of deciding which portions of the query stream should be boosted for regional or local content, such as "buying a digital camera" or "getting a driver's license," so that local content is credited more heavily in the ranking function.

Conversely, a query like "neural networks" does not need regional or local content boosts since they look for globally relevant results.

Siddharth built a Query Classifier based on regional intent using clickstream data and user query streams. Jonathan Siddharth retrained the search algorithm with the signals obtained from these analyses, which resulted in significantly improved relevance (based on Discounted Cumulative Gain).

Jonathan joined Powerset for seven months as a Ranking and Search Relevance Scientist the following year.

Powerset is a Natural Language Search Engine that reads and understands every sentence on the Web to answer inquiries from users in natural language. The parsing process included deep syntactic analysis and semantic analysis and markup, leveraging WordNet, FreeBase, and the like.


Powerset's Natural Language Search engine, which performs higher than Google, Yahoo!, and Live Search on the Discounted Cumulative Gain metric, was one of the top-performing engines on Wikipedia.

To produce a comprehensive ranking system, Siddharth analyzed the documents' deep syntactic and semantic content and combined them with keyword-based and Web graph-derived ranking features.

Several relevance tests on Mechanical Turk were also run to improve and optimize the ranking function. The results found by Jonathan Siddharth were used to improve the search engine's core ranking algorithm in the future. Because of this, the natural language search engine was also trained to rank results based on machine learning.

Powerset was acquired by Microsoft for a reported $100M and became a part of Bing.

While working with Powerset, Jonathan has also worked on a company of his own. He founded Infoaxe in 2007, the next-generation Search Engine.

In 2009, Jonathan Siddharth rebranded it into Flipora and made modifications.

From your web browsing history and your Facebook activity, Flipora automatically learns your interests and recommends websites based on what you are currently interested in. Flipora's ability and awareness to read its users’ moods allows it to make highly contextual recommendations based on what interests you at the moment.

Jonathan Siddharth rebranded it into Flipora

And for the third and last time, they rebranded it again as Rover. Rover app has been acquired by Revcontent, a self-funded company that powers content suggestions for publishers such as Forbes and The Atlantic. Jonathan Siddharth was the Senior VP of Technology until 2014. 


The Idea Behind Turing

The Idea Behind Turing

We are now in a world that prioritizes remote access. Because of the numerous advantages that completely remote teams provide, practically every IT business is now a remote organization.

Companies that have embraced remote work are now prospering and have a robust recruitment advantage.

Instead of recruiting from a narrow pool of developers near their headquarters, organizations that go remote have access to a global force of engineers. They can hire higher-quality employees and keep them for more extended periods.

But establishing a remote-first business isn't easy.

Companies that choose remote teams frequently face three challenges:

  • First, although finding mediocre developers is simple, finding Silicon Valley-caliber developers is difficult.
  • Second, evaluating and vetting remote talent to get the best applicant is complex. A typical CV provides organizations with a basic understanding of remote developers' core abilities and certifications. Information is scarce about the international colleges and firms where a developer has studied and worked.
  • Finally, even after locating the ideal remote development team, many businesses find it challenging to manage them. In other words, having a simple, compliant, and secure remote collaboration is difficult.
Jonathan Siddharth and Vijay Krishnan

While building their initial start-up, Rover, outside of Stanford in 2007-08, Jonathan Siddharth and Vijay Krishnan confronted all of these issues.

recognized the value and necessity

So, after Revcontent acquired Rover for about $30 million in 2017, the two decided to start something new based on their previous expertise, and was formed.

Turing's strategy is a vertically integrated solution that uses an AI-based platform to replace traditional IT service provider services. Turing has built the world's first and only Intelligent Talent Cloud, a dispersed workforce of engineers selected, vetted, matched, and managed entirely by software.

Who is Vijay Krishnan?

Who is Vijay Krishnan?

Vijay Krishnan is instrumental in the formation of Currently, Krishnan is the Co-Founder & CTO of Vijay graduated from Stanford University with a Master's Degree in Computer Science.

Before joining Turing, Vijay was an Entrepreneur in Residence (EIR) with Foundation Capital. Between 2008 to 2016, Krishnan was the Co-Founder and CTO of Rover. He has also worked at Revcontent as an SVP of Data Science. Vijay Krishnan has been a long-time friend and comrade of Jonathan Siddharth in pursuing endeavors.

Vijay Krishnan has over ten years of experience leading large-scale commercial machine learning efforts to solve problems like text categorization, personalized search, personalized content recommendations, CTR prediction, and eCPM maximization for personalized and contextual ad targeting and other business goals.

The Turing Timeline

The Turing Timeline

Turing was founded in 2014 while co-founders Jonathan and Vijay ran their previous AI business, Rover. There was a time when Rover was fast rising on the internet; the company was at a critical crossroads. Both thought it'd be successful with their Series A round of funding, but the VCs all turned them down since they didn't have a mobile app.

Their failure prompted a wild rush to recruit mobile talent, experts who could help construct their mobile app. Siddharth and Krishnan chose to look for work in labor arbitrage geographic locations. As a surprise, they discovered some incredible remote talent. The experience taught us one thing when the app was built: the best way to hire a talented workforce is to be able to source it from all over the world.

With Turing’s success, Jonathan Siddharth and Vijay Krishnan wanted a brand name associated with engineering excellence. On multiple levels, Alan Turing's name works. The Turing Award is the Nobel Prize for computer scientists and engineers. The Turing Prize name is a fantastic match because one of our objectives is to provide an opportunity for highly talented engineers to alter the world.

Alan Turing, the father of computer science and AI, quoted:

Jonathan and Vijay thought Turing was an excellent name because it is a core component of the tech industry. Like the Alan Turing Award, the term is also a reference to engineering excellence that everyone in tech and the world acknowledges.

For example, the Turing Test is a test for artificial intelligence in which we cannot detect the difference between the AI and a person if the AI is advanced enough. People are thrown at this problem by traditional outsourcing companies. Turing, on the other hand, employs the method and artificial intelligence.

Furthermore, the Turing co-founders want people to understand that at Turing, AI is not a means to destroy jobs; instead, it is a means to help people find them.

What Makes Turing Different from Other Outsourcing Marketplaces

Marketplaces typically cater to gig workers looking for short-term or part-time work, making it even more challenging for businesses looking for long-term projects and engagements.

While in Turing, the company pre-screens all developers for a Silicon Valley bar, saving employers a ton of time in the interview process: generally 50 hours each hire.

Advanced machine learning algorithms connect the appropriate engineers to the right roles, speeding up the hiring process even further. Customers have a 97 percent engagement success rate after being matched.

Turing has a long-term strategy with developers. They're developing the world's most developer-centric company, and they value the opportunity to help developers advance their careers. Turing draws the finest and brightest from all over the world because of this.

Obstacles Behind Companies Going Remote

We are now in a remote-first environment. Every business is vying for the advantages of remote technical expertise.

However, making the adjustment to remote work is difficult. This is because organizations must first have a big global pipeline to uncover excellent talent. Once this is established, they must screen remote applicants at scale without consuming all of their existing technical team's bandwidth.

Finally, they must guarantee that remote software development is simple, free of communication and time zone concerns, and compliant and secure.

Siddharth and Krishnan asked themselves a simple question: What if we could solve all of this with software? This is why Turing was built.

The Standards That Engineers Need to Pass To be On The Turing Platform

For a Silicon Valley bar, the company’s sophisticated vetting system for a Silicon Valley bar assesses worldwide developers. Engineers from Facebook, Google, and Microsoft collaborated on Turing's vetting engine. They employ extensive and constantly updated profound developer profiles to identify developer strengths and improvement opportunities, allowing them to conduct effective vetting. As a result, a Turing deep developer profile is far more comprehensive than a traditional résumé or LinkedIn profile.

Technical talents, soft skills, and seniority level are all factors in evaluating developers. CS foundations, systems design, experience in specific tech stacks, and raw coding aptitude are all included in the technical skills evaluation. Coding abilities are automatically assessed and stored in the cloud, making it simple for a hiring manager to analyze them later.

The whole process takes 2 to 5 hours and saves the developer time. Turing's use of machine learning improves the accuracy and efficiency of our vetting and matching procedures. The company’s verification process gathers tens of thousands of signals per developer, then integrates machine learning algorithms to match the appropriate developers with the right tasks.

Other Solutions

Turing is a fully prepared solution. Turing allows you to push a button to create your engineering dream team in the cloud and source developers. Turing’s intelligent talent cloud unifies sourcing, screening, matching, and quality control vertically. 

The company's post-match quality control addresses the issues of communication, transparency, performance management, and security, all of which are critical for a dispersed team's success. Currently, Turing is working on additional products to make it easier for larger teams to join Turing.

Turing and AI

Our cutting-edge machine learning and AI technology are evident in vetting and matching.


Turing’s powerful AI-powered vetting system develops a detailed developer profile for each engineer during the verification process. This is a thorough, comprehensive, and constantly updated picture of developers' strengths and development opportunities.

It's the most accurate portrayal of a developer's output worldwide. It goes beyond a standard CV or Linkedin profile by highlighting areas where developers thrive.


With Turing’s machine learning technology and enormous volumes of data, our deep matching algorithms make ideal matches between the appropriate engineers and the right jobs. 

Turing's Talent Cloud saves performance reports from each developer's previous managers. These assessments give feedback on their strengths as well as places for improvement. Turing can produce very accurate matches based on on-the-job performance data.

Turing's intelligent matching system is based on extensive, end-to-end information regarding job needs and developer skill sets. They use signals from our vetting engine and on-the-job performance data to establish granular matches.

The company’s profit comes in from the massive training data generated by Turing’s matching systems. They know which developers are most successful in specific jobs. As more firms worldwide utilize the matching engine, it becomes more innovative and wiser. It is estimated that Jonathan Siddharth’s net worth is around $14 million.

Finally, Turing utilizes machine learning to create models that weigh the many aspects of the job and the developer's in-depth profile to discover the ideal match that assures both client and developer success. Companies don't have to spend as much time interviewing engineers when suggestions give precise matches.

Turing’s Ambitious Vision

Bridging the talent-opportunity barrier and eliminating the geo lottery are found significant to speed up global growth. Right in front of our eyes, we are witnessing a generational transformation in how we work.

Jonathan Siddharth and his colleagues feel blessed to develop a platform at this transformation's core. In five years, Turing’s goal is to be one step closer to realizing the globe's latent human capabilities and helping to make the world a better place.

20 Pioneers of Artificial Intelligence & Machine Learning Then and Now

Personal Life

Personal Life

Jonathan Siddharth was engaged in 2019 to his girlfriend, and the couple tied the knot in 2020.

In 2022, Jonathan and Emily Siddharth are expecting their first child.

Jonathan and Emily Siddharth
Jonathan Siddharth was engaged in 2019 to his girlfriend

Key Takeaway

Jonathan Siddharth is expanding ways to cater to all sides that could benefit from his line of work. When a person goes up the ladder it is truly rare to see a person carrying another to places of opportunity.

But Siddharth’s work outcome has this goal in mind. How can we imitate Jonathan Siddharth? We start with his vision and mindset.

Here are three ways:

1. SAFE route: starting low and adding as you go

Taking the SAFE route stands for Simple Agreement for Future Equity. Jonathan recommends leveraging SAFE notes in the seed round since they are faster, easier to close, and can be used in a rolling fashion.

2. Utilize the constant stream of expert insights

Jonathan contends that one of the most significant advantages of parallel fundraising is its ability to gather investor feedback consistently and implement it in real-time.

Utilize the constant stream of expert insights

3. Recruit board members with investor superpowers

Recruit board members with investor superpowers

Build relationships with investors wisely. Optimize your target list to find people who can add more value than just money.