Summary
As a Research Engineer at Smarsh, you will be analyzing a wide range of unstructured communications data to address problems for our customers. The Research Engineer is responsible for the development and evaluation of new research ideas including development, support, and improvement of existing capabilities. Smarsh is looking for Research Engineers to help shape the frontiers of multimodal communications research. We are seeking candidates who will excel in fast-paced environments and deliver results with precision and finesse. The ideal candidate will be someone who’s passionate about exploring new technologies, navigating complex and impactful problems, and building high-performance research infrastructures.
The role will involve working with the Research Engineering team, collaborate with the Data Science team, and align with the Product Managers in analyzing complex communications data, generating insights, and creating solutions as needed across a variety of tools and platforms. The ideal candidate for this position will possess the ability to perform both independent and team-based research and generate insights across multimodal data sets with a hands-on/can do attitude.
The role offers a unique opportunity to get exposure to many problems and solutions associated with taking machine learning and analytics research to production. On any given day, you will have the opportunity to interface with business leaders, machine learning researchers, data engineers, platform engineers, data scientists and many more.
\n- Development of machine learning models and other analytics following established workflows, while also looking for optimization and improvement opportunities
- Data annotation and quality review
- Exploratory data analysis and model fail state analysis
- Attend and actively participate in planning, stand-ups, retros, and other team meetings and discussions
- Contribute to Research discussions
- Time Allotment:
- 70% Machine Learning model development and evaluation
- 20% interaction with stakeholders to understand modeling needs
- 10% Machine Learning model support
- Required Education and Experience:
- Bachelor’s degree in Computer Science, Applied Math, Statistics, or a scientific field with research emphasis
- 1+ year research experience working with data & analytics (including school)
- Experience working with Python
- Familiarity with one or more data science and machine/deep learning frameworks and tooling, including scikit-learn, H2O, keras, pytorch, tensorflow, pandas, numpy, carot, lightning, Nvidia NeMo
- Experience using Git, Linux/Unix, an IDEs
- Preferred Education and Experience:
- Master’s or Doctor of Philosophy degree in Computer Science, Applied Math, Statistics, or a scientific field
- 1+ year experience working with NLP, text analytics/classification, audio analytics
- Knowledge of transfer learning, including word embedding models (gloVe, fastText, word2vec) and transformer models (Bert, SBert, and GPT-x etc.)
- Experience in using various Large Language Models (LLM)
- Experience with natural language processing toolkits like NLTK, spaCy
- Knowledge of microservices architecture and continuous delivery concepts in machine learning and related technologies such as helm, Docker and Kubernetes
- Cloud computing (AWS, GCS, Azure)