You’ll be a core member of our Fast Forward Labs Research team.
As a Research Engineer, approximately half of your time will be spent supporting our clients through advising on thier ML goals, strategic challenges, and prototyping.
Additionally, you’ll be expected to read current Machine Learning (ML) and data focused research papers, and stay current on emerging capabilities in the field (we focus on data and ML but have wide interests).
You’ll also be expected to synthesize new solutions and algorithms in these areas, and to build prototype systems that demonstrate the feasibility of these approaches. You’ll contribute your written perspective to our published reports, and regularly explain machine learning techniques to a variety of audiences.
Who We Are
Cloudera Fast Forward Labs is an applied machine learning research group within Cloudera that helps organizations recognize and develop new product and business opportunities through emerging machine learning and AI technologies. We offer a research subscription service, consulting services and advisory services to companies in a wide set of industries.
We support our clients by helping them create powerful data products, help businesses capture the value of emerging data capabilities, and bring the lessons learned through that work to our research process.
We value thoughtfulness, curiosity, creativity, and diverse perspectives. This position is based in our Brooklyn office.
Skills and Qualifications:
This position requires a strong math background, with a particular focus on machine learning algorithms and comfort with quantitative analysis.
You should be quite familiar with leading practices in the machine learning lifecycle, including data preparation for analysis, ML model selection, training and validation. We also expect that you have experience with preparing ML models for production.
We expect you to be a strong programmer, and willing to embrace software engineering best practices. We primarily work in Python for research code but are always open to the best technologies for a given problem.
Throughout your career, you should have contributed to the Data Science body of knowledge and have a demonstrable portfolio of work (i.e. published papers or technical blog posts, ML products/apps and/or github repositories)
We often find in applied machine learning that the art of asking the right question is more of a challenge than finding the answer. We expect you’ll be enthusiastic and skilled at formulating the right questions and thinking strategically. Sometimes the simplest solution is the best answer.
Most importantly, this position requires excellent written and verbal communication. We expect you to be able to explain nuanced technical work to audiences that range from students to engineers to executives.