SigOpt is the optimization platform that amplifies your research. SigOpt takes any research pipeline and tunes it, right in place. Our cloud-based ensemble of optimization algorithms is proven and seamless to deploy, and is used by globally recognized leaders within the insurance, credit card, algorithmic trading and consumer packaged goods industries.
SigOpt was born out of the desire to make experts more efficient. While co-founder Scott Clark was completing his PhD at Cornell he noticed that often the final stage of research was a domain expert tweaking what they had built via trial and error. After completing his PhD, Scott developed MOE to solve this problem, and used it to optimize machine learning models and A/B tests at Yelp. SigOpt was founded in 2014 to bring this technology to every expert in every field.
Interested in helping push the state of optimization forward? SigOpt is hiring!
The SigOpt Team
We’re amassing all the right talent - with experience ranging from industry-leading optimal learning techniques to advanced predictive models, SigOpt has built the optimization framework of the future.
Scott has been applying optimal learning techniques in industry and academia for years, from bioinformatics to production advertising systems. Before SigOpt, Scott worked on the Ad Targeting team at Yelp leading the charge on academic research and outreach with projects like the Yelp Dataset Challenge and open sourcing MOE. Scott holds a PhD in Applied Mathematics and an MS in Computer Science from Cornell University and BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University. Scott was chosen as one of Forbes’ 30 under 30 in 2016.
Patrick has spent many years in the industry working at tech companies such as Facebook and Wish on systems that have scaled to tens of millions of users. Most recently, Patrick worked at Foursquare on passive local recommendations, and worked on the growth team designing and improving user growth experiments. Patrick holds a Bachelor of Mathematics in Computer Science and Pure Mathematics from the University of Waterloo.
Alexandra works on everything from infrastructure to product features to blog posts. Previously, she worked on growth, APIs, and recommender systems at Polyvore (acquired by Yahoo). She majored in computer science at Carnegie Mellon University with a minor in discrete mathematics and logic, and during the summers she A/B tested recommendations at internships with Facebook and Rent the Runway.
Olivia is a graduating Mechatronics Engineering student at the University of Waterloo and former SigOpt intern. She has experience in a variety of different fields including machine learning, hardware development as well as full stack engineering and has previously interned at Facebook and Lockheed Martin. After completing her degree she will join us full time in 2018.
Maneesh is a Silicon Valley veteran with both technical and customer facing experience. Prior to SigOpt, he worked at companies like Foundry Networks and ATI Graphics where he worked on, and ran, design teams developing high speed networking chips and GPUs; and at DataSift and Amplitude Analytics where he helped build and lead their Client Services teams, while helping the sales teams achieve 3-4x YoY revenue growth. Maneesh majored in Electrical Engineering and Biomedical Engineering at The Johns Hopkins University.
Born and raised in San Francisco, Kurtis has held various positions at companies such as Nordstrom and SAP focusing on Customer Success and Service. He enjoys exploring local restaurants and new musical artists. Kurtis holds a Bachelors of Science in Biology from the University of the Pacific.
Kevin has machine learning experience in both a research and an industry setting, and is primarily interested in Bayesian statistics and variational methods. He previously worked on the search ranking team at Google and received a Bachelors and Masters from UC Berkeley in EECS, where he worked on sparse models with application to text data.
Dan is a recent SF transplant who graduated from Loyola University Maryland with a major in psychology and minors in math and philosophy and earned a MS in Computer Science from Johns Hopkins University with a focus on artificial intelligence and machine learning. Prior to SigOpt, he did software engineering for the Department of Defense and Fortego, a government contractor, and built predictive models at Enova, a fintech firm in Chicago.
Steven possesses a unique set of experiences which qualifies him to effectively ensure that SigOpt’s users get the highest value from our optimization platform. Recruited out of college by Deloitte Consulting, Steven worked on and managed technology projects across a variety of industries. Transferring back to academia, he wrote software in pursuit of graduate mathematics research at CalPoly and the University of Southern California. Prior to joining the SigOpt team, he worked as a data scientist and software engineer for both consumer and enterprise technology companies. He is extremely excited about working with SigOpt’s users to help them fully comprehend our platform and get the most out of their research and technology pipelines. Steven holds a MS and BS in Mathematics from California Polytechnic State University, San Luis Obispo.
Mike studies mathematical and statistical tools for interpolation and prediction. Prior to joining SigOpt, he spent time in the math and computer science division at Argonne National Laboratory and was a visiting assistant professor at the University of Colorado-Denver where he co-wrote a text on kernel-based approximation. Mike holds a PhD and MS in Applied Mathematics from Cornell and a BS in Applied Mathematics from Illinois Institute of Technology.
Harvey is interested in stochastic optimization and machine learning. He obtained his Ph.D. in electrical engineering from Princeton University, where his doctoral studies focused on approximate dynamic programming, stochastic optimization, and optimal learning; with applications in managing grid-level battery storage. He also holds a B.S. in electrical engineering from University of Texas at Austin.
Ruben is an expert in machine learning and robotics with more than 10 years of experience in Bayesian optimization. He has published several papers in that topic in top journals and conferences, while developing software that is being used both in academia and industry. He is a professor of Computer Science at the University Center for Defense in Spain (currently on leave). Previously he was a research assistant at the Instituto Superior Tecnico in Lisbon and the University of British Columbia. Ruben holds a PhD in Computer Science and a MS in Electrical and Mechanical Engineering from the University of Zaragoza.
Ben is a graduate of the University of Pennsylvania where he studied computer science and management. In the past, he has worked as an engineer at Nest Labs and a product manager at Lyft. He is fascinated by artificial intelligence at a technical, product, and societal level.
Taylor is a recent graduate of Carleton University where he studied computer science with a minor in mathematics. His interests reside in machine learning and computer vision, and he would like go back to school to study one of these topics. In his spare time Taylor does rock climbing and artistic gymnastics.
Katharina is currently pursuing her PhD at the University of Freiburg from where she also obtained a MSc in Computer Science. She is interested in practical hyperparameter tuning, automated machine learning and exploring methods to model the performance of algorithms. In her free time she enjoys hiking and traveling.