Software is expanding faster than most teams can keep up with it. Codebases sprawl, systems accrete layers of legacy logic, and AI-driven architectures add yet another dimension of complexity. Even elite engineering organizations find themselves wrestling with slow builds, inefficient runtimes, and bloated execution paths. Over time, performance debt compounds quietly—until developers are forced into reactive firefighting instead of building what comes next.
For Saurabh Misra, this problem has never been abstract. It has defined his career.
When Misra talks about his professional journey, it doesn’t sound like a list of job titles. It sounds more like an evolution—one shaped by a persistent fascination with how intelligent systems behave under scale, and why they so often slow down just when they matter most. From building performance tooling at NVIDIA, to working on large-scale machine learning systems at Cresta, to optimizing Instagram’s recommendation pipelines at Meta, Misra has spent years operating at the intersection of intelligence, infrastructure, and speed.
Again and again, he saw the same pattern emerge.
“Brilliant engineers build correct systems,” Misra says. “But correctness is where optimization usually stops. Performance becomes an afterthought—until it becomes a crisis.”
That realization eventually led him to found Codeflash, an AI-driven platform designed to do something deceptively simple: make existing code run faster without changing what it does. But Codeflash is less a pivot than a culmination of the ideas Misra has been refining for years.
Learning Where Systems Break
Misra’s grounding in performance began early. He earned his undergraduate degree from IIT (BHU) Varanasi, where he developed a strong foundation in electronics and systems engineering. He later went on to Carnegie Mellon University, completing a Master’s degree in Electrical and Computer Engineering. At CMU, he was awarded the James R. Swartz Entrepreneurship Fellowship—an honor given to just 12 students out of roughly 7,000—recognizing not just technical excellence, but an aptitude for building real-world impact.
At CMU, Misra also worked as a research assistant, gaining exposure to the academic side of machine learning and systems research. But it was in industry, he says, where the performance problem revealed its full cost.
At NVIDIA, Misra worked close to the hardware–software boundary, where inefficiencies are impossible to ignore. Later, at Cresta, he helped build and scale machine learning systems for enterprise customer support, eventually serving as a Staff Machine Learning Engineer. There, he led end-to-end efforts across modeling, deployment, and product engineering—seeing firsthand how small inefficiencies multiplied across production systems.
By the time he joined Meta as a Software Engineer in Machine Learning, Misra was operating inside one of the most performance-sensitive environments in the world. His work on Instagram’s recommendation systems focused on surfacing more relevant friends and accounts, directly contributing to increases in daily active usage. At that scale, even minor slowdowns translate into real costs—compute, latency, and energy.
“You can’t hide from performance when your system touches hundreds of millions of users,” he says. “Every inefficiency shows up somewhere.”
From Observation to Obsession
Despite the sophistication of the teams he worked with, Misra noticed something missing. Performance optimization was episodic—handled through audits, rewrites, or last-minute heroics. It wasn’t continuous. And it certainly wasn’t automated.
That gap became an obsession.
In 2023, at a hackathon, Misra demonstrated that large language models could do more than generate code—they could optimize it. By automatically rewriting parts of LangChain, a widely used open-source AI framework, the system delivered measurable performance improvements without altering functionality. The response surprised even him.
“People were shocked that a model could improve existing code, not just produce new code,” he recalls.
That weekend planted the seed for Codeflash.
Making Performance Continuous
At its core, Codeflash reflects Misra’s belief that speed is a form of intelligence. The platform integrates directly into development workflows and continuously optimizes Python code as it evolves—finding faster execution paths while preserving correctness.
“When you install Codeflash, every new line of code you write can be optimized from that point forward,” Misra explains. “The future isn’t just AI that writes code. It’s AI that designs efficient code.”
Since its launch, Codeflash has worked with open-source projects and AI-heavy companies including Hugging Face, vLLM, and Roboflow, delivering reductions in latency and compute cost. The company’s outcome-based pricing reflects Misra’s pragmatic mindset: value is measured in results, not promises.
But what excites him most is not any single optimization—it’s the idea of performance as a living system.
“Most optimization happens once,” he says. “But performance isn’t a project. It’s a culture. Codeflash runs every day, learning from your code and from itself.”
In one internal debugging session, Misra and his team discovered that Codeflash had already generated a more efficient version of its own logic. “That was the moment it clicked,” he says. “The system had literally become faster than the people who built it.”
Efficiency as a Moral Imperative
For Misra, performance isn’t just about developer productivity. It’s about sustainability.
Data centers already account for a meaningful share of global electricity consumption, and AI workloads are accelerating that trend. Every wasted computation translates directly into energy use. From Misra’s perspective, inefficient software isn’t just a technical liability—it’s an environmental one.
“Milliseconds turn into megawatts at scale,” he says. “If companies wrote optimized code from day one, the environmental impact of computing would be dramatically lower.”
This belief aligns with broader industry data. Reports from organizations like the International Energy Agency and the Consortium for Information and Software Quality have highlighted how inefficiency—both in energy use and software quality—now costs trillions of dollars annually. Misra sees Codeflash as one small but meaningful step toward reversing that trend.
Redefining Intelligence
Despite the current obsession with ever-larger models, Misra believes the next phase of AI will be defined by restraint rather than scale.
“The industry is chasing size,” he says. “But nature figured this out long ago. True intelligence is efficient.”
It’s a conviction shaped by years inside complex systems—and by watching what happens when they grow faster than they’re optimized. Today, as Founder and CEO of Codeflash, Misra is building toward a future where optimization itself becomes automated, continuous, and invisible.
“The holy grail is eliminating slow software entirely,” he says. “A world where AI ensures that all code is always optimal—expert-level by default.”
For someone who has spent his career watching systems strain under their own weight, that vision feels less like ambition and more like inevitability.
Saurabh Misra is a technologist and entrepreneur based in San Francisco. He is the Founder and CEO of Codeflash.ai and has previously worked at Meta, Cresta, NVIDIA, and Carnegie Mellon University. His work spans machine learning, large-scale systems, and software performance, with a consistent focus on building faster, more efficient, and more sustainable technology.
LinkedIn: https://www.linkedin.com/in/saurabh-misra/
