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Research Engineer: Machine Learning

Job ID: 23-04135
Job Title: Research Engineer: Machine Learning
Duration: Longterm Contract
Location: Burlingame, CA
Pay Range: $70-$80/hr
Intelliswift Software Inc. conceptualizes, builds, and supports the world's most amazing technology products and solutions. Our team of rich experts from diverse backgrounds contributes to making Intelliswift one of the most reliable partners in IT and Talent solutions. We specialize in delivering world-class Digital Product Engineering, Data Management and Analytics, and Staffing Solutions services to Fortune companies, SMBs, ISVs, and fast-growing startups.

Job Description: (contingent worker) Job title: Research Engineer: Machine Learning
OnSite Burlingame, CA

Description:
We are looking for a skilled and motivated machine learning research engineer, to develop and
apply state-of-the-art machine learning techniques for wristband/hand-based gesture inputs to
deliver seamless experiences for humans — specifically, to develop online learning and
reinforcement learning techniques that improve gesture recognition models in real time to
achieve all-day high performance.

Responsibilities:
• Develop or apply state-of-the-art reinforcement learning and online
learning algorithms that improve model accuracy in real-time for everyday usage.
• Collaborate with research engineers and scientists in building software
platforms, for both data analysis and real-time experience of human-system interactions;
deploy and evaluate the online algorithms in real usage.

Minimum qualifications:
• Has a BS with 4+ years of experience or MS with 2+ years of experience
or PhD with 1+ years of experience in Machine Learning, Robotics, Statistics,
Biomedical Engineering, Neuroscience, Computer Science, Signal processing or a related
area.
• Has 2+ years experience coding in Python, Pytorch, with proven track
record of implementing and deploy Machine Learning algorithms
• Has 1+ years experience with deep learning

Preferred qualifications:
• Has 1+ years of experience in any of the following domains: (deep)
reinforcement learning, online learning, bayesian optimization (active learning) or semisupervised learning
 
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