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As a Fintech company where Machine Learning (ML) is a key feature, our operations rely heavily on machine learning models across business decisions and customer experiences. Therefore, ensuring the health and scalability of our machine learning systems is critical. To guarantee the success of machine learning systems, work needs to be done to transform ML models into high-performance, production-level code. This includes not only implementing sophisticated machine learning algorithms but also robustness monitoring, system logging/alarming, and DevOps.
Job Responsibility:
Design, build, and launch efficient and reliable machine learning (ML) models to drive business impact
Train and validate state-of-the-art multi-modal, multi-task deep learning models as well as statistical models, considering use-case, complexity, performance, and robustness
Demonstrate end-to-end understanding of applications and develop a deep understanding of the “why” behind our models & systems
Partner with product managers, tech leads, and stakeholders to analyze business problems, clarify requirements, and define the scope of the systems needed
Work closely with data platform teams to enable robust, scalable batch and real-time data pipelines
Drive high ML and engineering standards on the team through mentoring and knowledge sharing. Drive engineering best practices around code reviews, automated testing and monitoring
Requirements:
MS degree or PhD degree in Computer Science or a related technical field
4+ years of experience in ML engineering
Strong programming skills in Python and data engineering skills
Extensive knowledge of machine learning algorithms
Hands-on experience on architectural patterns for large-scale software applications
Industry experience building and productionizing machine learning systems
Strong oral and written communication skills
Nice to have:
Experience in NLP or CV is a plus
What we offer:
healthcare
internet/cell phone reimbursement
a learning and development stipend
opportunities to collaborate with and travel to our Palo Alto HQ and Bangkok Site
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