ROLE : Machine Learning Engineer

LOCATION : Pittsburgh, PA

PRIMARY DUTIES AND RESPONSIBILITIES

• Create machine learning models and applications. Use your strong multiple programming languages to lead the design and development of the next generation machine learning algorithms that will drive operational improvements across the healthcare continuum of care and dramatically improve insights for all our stakeholders both internal (analysts, business leaders, etc.) and external (clinicians, health system CEOs and CIOs, etc,).
• Select appropriate databases and data representation methods that ensure successful testing of machine learning models and are representative of our client's data.
• Perform statistical analysis and fine-tuning using test results.
• Run machine learning tests and experiments.
• Keep abreast of developments in machine learning and statistics.

EXPERIENCE

• 8 Years of total software engineer experience of which 4+ years of experience building functional Client applications for prediction, utilization (e.g., commercial products or government projects), NLP.
• Previous experience in using Hadoop ecosystem Client tools (SparkML, Mahout, etc.)
• Experience validating software through industry accepted testing strategies
• Experience working in an Agile development environment
• Proven experiences on delivering distributed systems and services in a production setting
• A portfolio of relevant publications or open-source projects to share with us
• A desire to keep up with the field by attending or publishing at relevant conferences (ACL, EMNLP, NAACL-HLT, ICML, NIPS, etc.

SKILLS

• Graduate-level expertise (or equivalent industry experience) in machine learning, natural language processing, or related field
• Expert knowledge in implementing Client systems at scale in Python, Java, Scala, SparkML, or C/C++ (i.e., not just R or MATLAB)
• Be responsible for the architecture, design, development, and operations of large-scale systems designed for machine learning. These may include, but not limited to, data management systems, data engineering workflow systems, distributed compute systems, and their web portal & web service components
• A strong mathematical background in statistics and machine learning. Understands data structures, data modeling, and software architecture.