Machine Learning Engineer

August 23

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Bazaarvoice

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eCommerce • Retail • user-generated content • Brands • Shopper Advertising

1001 - 5000

💰 $6.7M Post-IPO Equity on 2012-11

Description

• Research, design and prototype intelligent systems with the aim of enhancing online shopper experience. • Assist in research prototypes and develop them into fully-fledged AI software that are ready to be delivered to our clients. • Participate in active maintenance of models and ML-pipelines • Maintain and improve legacy models and ml pipelines suggesting and implementing changes as appropriate • Participate in code reviews ensuring that released code maintains our standards of quality and engineering excellence • Keep up-to-date with the latest papers in artificial intelligence and machine learning to propose solutions for real problems in e-commerce, particularly as it pertains to optimizations in productionalization • MLops: Build and help maintain infrastructure to support the evolution of our shopper interaction toolset. • Mentor other engineers and share relevant knowledge. • Troubleshoot, test, and debug to your heart’s content. • Being able to identify key metrics and experience building and responding to automated monitoring systems. • Ad-hoc problem solving based on client needs. Our white glove service means we often have to implement solutions to client specific problems in robust and well designed ways

Requirements

• At least 2 years of real-world experience implementing Machine Learning software • At least 3 years of experience (in total) implementing production level software • Proficient in Python and/or C/C++, with an interest in learning new languages • BSc (MSc or PhD preferred) in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering, Physics, or a related discipline, with (at minimum) graduate-level courses in machine learning, or equivalent practical experience. • Strong research experience in machine learning, preferably in one or more of the following (in no particular order): reinforcement learning, natural language processing, LLMs, recommendation and/or ranking systems, deep generative models, representation learning, AI interpretability, domain generalization, meta-learning, computer vision, deep neural network architectures. • Proficient in deep learning frameworks like Tensorflow, PyTorch, etc. and scientific computing packages like NumPy. Able to implement an algorithm as described in an academic paper using these frameworks in quality code. • Strong computer science background, with experience in object-oriented programming, systems design, data structures and algorithms. • Familiarity with source control (Git) and Unix systems, including shell scripting. • Good intuition for applying AI theory to make business-oriented products with minimal guidance. • Communicate to introduce honesty and clarity (avoiding buzzwords and jargon) to experts in multiple disciplines. Demonstrate a mature understanding of the current possibilities and limitations of AI research. • Curious, constantly looking for better ways to build things and excited to learn about emerging technologies.