Machine Learning Engineer (Remote)

The company is an applied behavioral research company working at the intersection of ML, social sciences, and recommendation systems / Prediction – as – a – service. The company enables businesses to build privacy preserving recommendation and behavioral technologies competitive to big tech without the use of interpretable raw customer data.

We’re looking to develop the next generation of privacy preserving machine learning products that understand and predict behavior at scale. Our products and research teams need to handle information at a massive scale across a number of unstructured dimensions. We’re looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, privacy, artificial intelligence, and NLP.

Job Qualification:

● Bachelor’s degree or equivalent practical experience.

● 5+ years of experience with software development in one or more programming languages, and with data structures/algorithms.

● 5+ years with two or more languages/softwares included but not limited to: Python, Apache, Presto, R, ML/optimization, Scala

● 5+ years of experience in one or more of the following areas: machine learning, recommendation systems, pattern recognition, NLP, data mining or artificial intelligence

● 5+ years of experience with ML/AI algorithms and tools, deep learning and/or natural language processing.

Responsibilities:

● You enjoy partnering with data science teams to deploy and scale advanced algorithms

● You strive to write elegant code, and you’re comfortable with picking up new technologies independently

● You enjoy collaborating with colleagues/partners internally and externally

● You are passionate about building intuitive data models and an expert in distributed data processing patterns

● You are comfortable working in a rapidly changing environment with ambiguous requirements. You are nimble and take intelligent risks

What you will do:

● Engineer efficient, adaptable, and scalable data pipelines to process structured and unstructured data

● Maintain and rethink existing datasets and pipelines to service a wider variety of use

cases

● Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules-based models

● Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)