| Job Announcement – UtopiaCompression
Machine Learning/AI Scientist
Posted 07/14/03
UtopiaCompression (UC) is an early-stage, intelligent imaging solutions company. UC’s core offering is an intelligent image compression technology, which was recognized in 2002 as one of the top emerging technologies in the US by the National Institute of Standards and Technology (http://jazz.nist.gov/atpcf/prjbriefs/prjbrief.cfm?ProjectNumber=00-00-4936).
Basic Qualifications
UtopiaCompression is looking for a highly qualified candidate with extensive experience and knowledge in machine learning, data mining and knowledge discovery. The candidate is required to have an MS or Ph.D. in the areas mentioned above from a highly reputable university. Post-doctorate and/or industry experience is strongly preferred. The ideal candidate will be thoroughly versed in the latest research, methods, developments and theories in machine learning and data mining as well as possess in-depth experience applying them to commercial, scientific or industrial applications. The candidate is also required to be a visionary, highly creative and a great problem solver capable of proposing solutions to multiple problems in parallel, and mentoring and guiding R&D engineers in developing and implementing the solutions. Permanent residents or US citizens are preferred.
This position is ideally suited for full-time employment, but part-time, contract and contract-to-hire arrangements may also be considered.
Skills & Qualifications
1 – In-depth knowledge and experience in statistical analysis, reasoning and learning (e.g., Bayesian learning, estimation maximization and maximum likelihood algorithms, and feature extraction problems), (statistical) combinatorial optimization and learning (e.g., simulated annealing, genetic programming), neural networks, inductive and rule generation learning, fuzzy reasoning, (numeric) decision tree learning, search methods, (image) data mining and understanding, etc. Candidates are expected to have knowledge and working experience in various learning regimes. For instance, in the case of layered neural nets dexterous familiarity with the back propagation algorithm, radial basis functions, etc., in decision tree learning working experience in information gain measure, category utility function, tree pruning, etc.
2 – Dexterous familiarity with various machine learning and statistical software tools.
3 – Fluency in software analysis, design and development using C programming environment. Candidates must be well versed and experienced in C. Working experience in C++ (and Java) is a plus.
4 – Knowledge and working experience with image compression techniques, and image analysis and processing is a big plus.
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