Sr. Engineer, Machine Learning

Vulcan Inc. is looking for an intellectually curious and self-motivated machine learning engineer to help us tackle challenging problems in conservation, climate change, bioinformatics, and artificial intelligence.

At Vulcan, you’ll join a top-notch technology team that’s dedicated to solving some of the world’s toughest problems. Your code will make a difference: you’ll have the opportunity to work on projects that are making a real impact on the lives of others.

As a Senior Machine Learning Engineer, you’ll also have the opportunity to help develop and maintain a powerful data and computing infrastructure for machine learning. You will be also be able to hone your machine learning and data science skills through focused experimentation and problem-directed research.

Successful applicants should have a strong foundation (typically PhD level) in machine learning, applied mathematics, predictive modeling, bioinformatics, statistics, and/or artificial intelligence. We look favorably upon extensive work experience and publishing demonstrating application of your research.

ESSENTIAL DUTIES AND RESPONSIBILITIES

• Apply machine learning to help address challenging problems in conservation, ecology, climate change, bioinformatics, data science, and artificial intelligence.
• Conduct statistical analyses to learn from unstructured, real-world data.
• Design algorithms that combine human & machine workers to train high quality ML models.
• Further advance machine learning through focused experimentation and problem-directed research.
• Contribute to the codebase. Adapt algorithms and models to production services and components.
• Build reusable tools to aid in rapid iteration and development.
• Help develop and maintain a data and computing infrastructure.

QUALIFICATIONS

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Knowledge, experience, skill, and/or ability

Required

• Extensive experience solving analytical problems using quantitative approaches using machine learning methods.
• Strong working knowledge of basic predictive modeling and machine learning concepts
• Experience with a deep learning framework such as Theano, Keras, Lasagne, Caffe, or Torch, and machine learning libraries including scikit-learn.
• Experience with MATLAB, Python or other languages for technical computing, as well as Cloud Computer technologies such as Hadoop, Storm, Kafka, Spark, and Zookeeper.
• Solid knowledge of statistical classifier models and of best practices in attribute selection, dimensionality reduction, runtime performance optimization.
• Comfortable in cluster and GPU computing environments.
• Can easily translate new algorithms into efficient code.
• Aptitude with continuous integration and automated testing.
• Comfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources.
• A flexible analytic approach that allows for results at varying levels of precision.
• The ability to work independently and thrive in an environment with plenty of freedom and lots of responsibility.
• Excellent practical judgment. You should know how to effectively allocate your time when solving hard problems.
• Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner.
• Experience with Agile software developments practices and tools.
• Commitment to staying on top of the latest AI and machine learning research papers, and interested in pushing the boundaries.
• Experience with data visualization a plus.

Required computer skills

• MS Office (Word, Excel, Outlook)

• Ph. D in a relevant technical area (machine learning, computer science, computational linguistics, physics, mathematics, statistics, or related field), or a Master's Degree in a relevant field and 4 plus years experience in a relevant role, OR equivalent combination of experience and education.

Special remarks regarding work environment, if applicable

• Travel estimated to be 10%