We're seeking a mid-level Research Engineer to help build out some very exciting capabilities. This is an applied research and development position. The Research Engineer will work on machine learning, computational linguistics, graph analytics, predictive analytics, knowledge representation, and generally advanced analytics for recruiting-oriented AI. This role requires an ability to recommend and implement appropriate modeling approaches and communicate and share findings. This position reports to the Head of AI.
- Apply quantitative, analytical, and creative skills to design, develop, and test projects independently and jointly with other team members to deliver powerful functionalities for generating recruiting automation systems.
- Explore applied research ideas and formulate and execute R&D projects in machine learning, computational linguistics, knowledge representation, advanced analytics, predictive analytics, and graph analytics.
- Research, design, and develop novel algorithms.
- Collaborate with architects, software developers, and product management to design and program innovative strategic and tactical solutions that meet market needs with respect to functionality, performance, reliability, realistic implementation schedules, and adherence to development goals and principles.
- Take ownership of certain parts of our platform, especially ones you pioneer.
- Integrate our platform with open-source and other third-party components in the machine learning, computational linguistics, advanced analytics, and unstructured analytics domains.
- Gather and determine requirements for new features.
- Develop and document intellectual property.
- Effectively communicate product architectures, design proposals, and discuss option tradeoffs with AI management, engineering management, and team members.
- Help to train new and existing talent in research, engineering, and solutions.
- Knowledge of machine learning fundamentals and implementation.
- Experience in two or more of the following areas: machine learning, statistical modeling, computational linguistics, optimization, algorithms, analytics software development, big data analytics, natural language processing, data mining, or predictive analytics.
- Strong statistical/math/analytical abilities and exceptional problem solving skills.
- Expertise in object-oriented design methodology and software development in Java.
- Solid computing background with a thorough understanding of the fundamentals of computer science, data structures, and algorithms.
- Excellent communication, teamwork, and technical writing skills.
Required Education and Experience Level:
- Master’s degree, or equivalent, in Computer Science. Exceptional candidates with Statistics, Math, Linguistics, Operations Research, Engineering, or related fields may also be considered.
- At least 3 years of relevant experience in progressively more responsible research engineering roles.
Preferred, But Optional, Skills:
- Experience with deep learning frameworks such as TensorFlow, TensorBoard, Keras, or dl4j.
- Significant experience writing code is preferred.
- Staying in touch with the latest findings, trends, best emerging practices, and techniques in machine learning, data mining, search, graph analytics, computational linguistics, knowledge representation, big data analytics, and/or statistics.
- Java threading, concurrency, locking, and using shared resources.
- Experience developing semantic search capabilities.
- Understanding of graph analytics and graph algorithms.
- Exposure to rule mining, semantic vector spaces, entity recognition, dialogue systems, and text analytics.
- Experience in using semantics, e.g. ontologies, knowledgebases, knowledge graph approaches.
- Strong in linear algebra and/or multivariate statistics.
- Experience with both approximate algorithms and exact analytical methods.
- Experience working with rules engines or the semantic web.
- Strong in regular expressions.
- Expertise in statistical modeling techniques such as clustering, classification, regression, recommenders, dimensionality reduction, feature creation, feature selection, and advanced statistical models in general.
- Knowledge of indices such as Solr, Lucene, or ElasticSearch.
- Evaluation of fits, error metrics, and model diagnostics to assess model performance.
- Experience exploring large datasets, identifying key patterns and structures that can be exploited or modeled to create features and improve forecasting performance.
- Experience as a developer on large software development projects.