Turing

The Invisible Engine Behind the AI Race

Somewhere in a San Francisco office, a software engineer at OpenAI is writing a note. Not code - a note, explaining why one AI-generated solution is better than another. That note gets fed back into a model. The model learns. The model gets smarter.

Who sourced and vetted the engineer writing the note? Who built the pipeline that captures the feedback, structures it, and delivers it at scale across thousands of domain experts? Who deployed the system to make all of it work in production?

Probably Turing.

Turing is, by any reasonable measure, one of the most important companies in AI that most people haven't heard of. It sits exactly at the intersection where intelligence meets infrastructure - the hidden plumbing that makes frontier models better and enterprise AI actually ship. In March 2025, the company closed a $111 million Series E at a $2.2 billion valuation, doubling its value in a single funding cycle. Revenue is running at $300 million annually. The company has been profitable for over a year.

A Crisis of Geography and Trust

The story starts not in a conference room, but in a panic. In 2014, Jonathan Siddharth and Vijay Krishnan were running Rover, a previous AI startup, and growing fast. Too fast. They needed software engineers, and the local talent market wasn't keeping up with demand. Silicon Valley, as it turned out, had limits.

So they hired internationally. Engineers in Ukraine, Serbia, China. The apps shipped. Rover scaled and was ultimately acquired by Revcontent. The lesson stuck: the world was full of exceptional engineering talent. Companies just didn't know how to find it, vet it, or trust it.

The traditional answer was a staffing agency. A recruiter sends resumes. Hiring managers wade through candidates. Weeks pass. Bad hires happen. It's a broken process masquerading as a market.

Siddharth and Krishnan - both Stanford AI alumni - had a different idea. What if hiring were treated as a data and AI problem rather than an HR problem? What if you could analyze 20,000 data points per candidate, conduct automated technical assessments, run live AI-powered interviews, and deliver a shortlist of pre-screened, pre-matched engineers within four days?

Stanford, Startups, and a Very Big Wager

Turing was founded in March 2018 in Palo Alto. The thesis was audacious: build the world's largest, smartest talent marketplace - not by listing developers on a directory, but by continuously vetting, ranking, and matching them using machine learning at scale.

Jonathan Siddharth (CEO) and Vijay Krishnan (CTO) had already built and sold companies together. They knew what good engineering looked like, and they knew the market was undervaluing it. Siddharth, who grew up in India, had a personal understanding of what geography did to opportunity. Krishnan, whose early work on large-scale text classification at Yahoo became patented methodology, had the technical credibility to build the AI side of the platform.

Foundation Capital led a $14 million seed round in 2019. WestBridge Capital followed with a $32 million Series B in 2020. By December 2021, Turing hit unicorn status at a $1.1 billion valuation during its Series D. The growth was real, and it was accelerating.

Three Businesses. One Platform. Zero Overlap.

By 2025, Turing had evolved well beyond a hiring platform. The company now runs three distinct but interconnected business lines.

The first is the original: the Intelligent Talent Cloud. This is the engine that vets engineers - through automated coding tests, LLM-based live interviews, and behavioral analysis - and matches them to companies needing technical talent. Forty-eight-hour shortlists. Pre-screened. Pre-matched. The deep vetting engine analyzes over 20,000 data points per candidate and the system improves every time a hire is made.

The second is Turing AGI Advancement. This is where Turing's trajectory got genuinely unusual. In 2022, OpenAI came to Turing not to hire developers but to buy training data. Specifically, they wanted human experts - coders, mathematicians, scientists - to review model outputs, rank them, write better alternatives, and flag errors. The feedback loops that teach a model to reason better. Turing had the infrastructure and the vetted talent network to do this at scale. They built dedicated pipelines: coding datasets, STEM benchmarks, multimodal training data, safety annotations. They created SWE-Bench++ and Code Review Bench. They built reinforcement learning environments. Anthropic, NVIDIA, and Gemini followed OpenAI in.

The third is Turing Intelligence. This is where learnings from training frontier models get turned into enterprise AI systems. Turing embeds AI-native talent pods directly into client workflows - teams that build and ship production-grade agents for underwriting, audit preparation, onboarding, and customer support. The results are quantifiable: 45% reduction in audit cycle times, 60% faster mortgage approvals.

Powering all three is ALAN, Turing's proprietary fine-tuning platform. ALAN accelerates model evaluations, supervised fine-tuning, RLHF workflows, preference-pair generation, benchmarking, data capture, and agent development. It is, in essence, the factory floor of Turing's AI operations.