The Future of Deep Learning is Sparse.The Future of Deep Learning is Sparse.

The Future of Deep Learning is Sparse.The Future of Deep Learning is Sparse.


It all started in Cambridge, Massachusetts.

While mapping the neural connections in the brain at MIT, Neural Magic’s founders Nir Shavit and Alexander Matveev were frustrated with the many limitations imposed by GPUs. Along the way, they stopped to ask themselves a simple question: why is a GPU, or any specialized hardware, required for deep learning?

They knew there had to be a better way. After all, the human brain addresses the computational needs of neural networks by extensively using sparsity to reduce them instead of adding FLOPS to match them.

Based on this observation and years of multicore computing experience, they created novel technologies that sparsify and quantize deep learning networks and allow them to run on commodity CPUs – at GPU speeds and better. Data scientists no longer have to compromise on model design and input size, or deal with scarce and costly GPU resources. Their ground-breaking discovery became the foundation of Neural Magic.

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The Company’s Vision
Together with our community, we are on a mission to bring software and algorithms, rather than specialized hardware, back to center stage in machine learning infrastructure.

Investors


Neural Magic Team


Brian Stevens Chief Executive Officer

Former CTO of Red Hat and Google Cloud, disruptor, biker.
Nir Shavit Co-Founder

MIT professor, innovator, tennis player.
Alexander Matveev CTO & Co-Founder

Former MIT research scientist, specialized in multicore algorithms and systems for AI.
John O'Hara Vice President Engineering

Engineering and operations lead, skier, mentor to engineers and peers.
Jeannie Finks Head of Customer Success

Model optimization, deep learning economics, yoga, killer sangria, adventure travel.
Saša Zelenović Head of Marketing

Go-to-market expert, helping data scientists experience the power of Neural Magic. Weekend beekeeper.
Mark Kurtz Director of Machine Learning

Research and engineering, model optimizations, volleyball, woodworking
Dan Alistarh Principal Research Scientist

Algorithms, math, tennis---not necessarily in that order.
Bill Nell Software Engineering Manager

JITs, compilers, program analysis, HPC.
Tyler Smith Software Engineering Manager

UT Austin PHD, ETH Zurich postdoc. Specializing in linear algebra computations & theoretical bounds on data movement.
Dan Huang Software Engineering Manager

A constructor (DevOps) and a breaker (QA). Passion for automation, quality, cooking and mushroom hunting.
Andy Linfoot Principal Software Engineer

JITs, Numerics, and HPC alchemy.
Tuan Nguyen Senior ML Research Engineer

Building software and deep learning models for fun and profit.
Michael Goin Product Engineering Lead

Data-driven optimization, AI evangelist, modular synthesis, baking.
Sage Moore Software Engineer

Operating systems, scheduling, high performance computing, biking, powerlifting.
Kierstin Darragh Business Development Lead

Computer vision at the edge, cooking, true crime podcasts.
Benjamin Fineran ML Engineering Team Lead

Model sparsifier, problem solver, sports enthusiast.
George Ohashi Full Stack Engineer

Curious about how and why things work. Vegan + fish, book reader, classical guitar player, fan of biology, history and sports
Rahul Tuli ML Engineer

Intelligent systems, pattern recognition, NLP, badminton, tacos.
Daniel Campos ML Research Engineer

NLP aficionado, PhD Candidate UIUC, kiteboarder, winemaker.
Domenic Barbuzzi Software Engineer in Test

Proclivity for automation and debugging. Video game player, hobbyist photographer, and enthusiast of all things tech.
Konstantin Gulin Senior ML Engineer

Data curious, lifelong student, kitchen experimenter, viticulture enthusiast, backpacker.
Damian Bogunowicz ML Engineer

A curious human being, especially fond of AI, financial markets, and being less wrong every day. Lifelong learner, fitness enthusiast, and self-proclaimed food aficionado. Life goals: Work hard, retire early, buy a cabin in the Alps, drink wine, eat cheese, study math and philosophy.
Eliza Wszoła Software Engineer

ETH Zurich Ph.D. in hardware-efficient manycore CPU implementations of machine learning models. Amateur visual artist and collector of random trivia from the internet.
Alexandre Marques ML Research Team Lead

Scientist/engineer specialized in computational models and algorithms. Bridging the gap between fundamentals and applications, having fun along the way.
Ricky Costa Engineer

Lover of open source software. Dark web surfer. I hack things.
Rob Greenberg Product Manager

Data, customer obsession, CNNs, music production, skiing, biking, cryptocurrencies, NY Sports.
Derek Kozikowski Senior Software Engineer in Test

Software quality maven, test automation developer, gardener, and woodworker.
Jay Marshall Head of Global Business Development

Amazonian and Xoogler focusing on customers, partners, messaging, farming, music, and mixology.
Matthew Helmers Senior Account Executive

Making customers wildly successful. Waymaker. Outdoor enthusiast.
Danny Guinther Full Stack Engineering Manager

Hacker, cloud builder, mentor, ANN enthusiast.
Rob Fitzgibbon UX Lead Engineer

Passionate about good UX, fascinated by analytics, avid coffee drinker.
Chibu Ukachi MLOps Engineer

Spellbounded by the intersection of Cloud and AI, fan of fresh smoothies, traveller and always smiling.
Corey Lowman Senior Machine Learning Engineer

Dreams in code. Love learning, food, and music. Getting better at dancing.
Varun Sundar Rabindranath Software Engineer

Fascinated by Compilers, ML, HPC and Embedded Software. Open Source Believer. Amateur Potter.
Benjamin Blue Software Engineer

Hobbiest side project and writer dude. Love to read. Eternal disc golf player and fan.
Derrick Mwiti ML Developer Advocate

Experienced in data science, machine learning, and deep learning with a keen eye for building machine learning communities.

Join Our Team


We are looking for talented and ambitious team members to help us shatter the hardware barriers holding back the field of machine learning. Are you ready to challenge the norms?