Machine Learning Scientist
| Company | INflow Federal |
|---|---|
| Location | Arlington, VA, USA |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Bachelor’s, Master’s |
| Experience Level | Senior |
Requirements
- Bachelor’s degree plus 7-10 years experience OR a Masters Degree plus 5 years of experience
- Experience with ML fields, e.g., natural language processing, computer vision, statistical learning theory
- Hands-on experience with Natural Language Processing (NLP), Large Language Models, text embedding, semantic query, use of generative AI for text, and retrieval augmented generation (RAG)
- Familiarity with data preprocessing, feature engineering, and model evaluation techniques essential for machine learning projects
- Strong understanding of various machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and neural networks
- Experience with version control systems like Git, enabling effective collaboration and code management
- Experience in an ML engineer or data scientist role building ML models
- Experience writing code in Python, R, Scala, Java, C++ with documentation for reproducibility
- Experience using Apache Spark/Databricks distributed compute environments for AI/ML workloads
- Experience handling petabyte size datasets, diving into data to discover hidden patterns, using data visualization tools, writing SQL, and working with GPUs to develop models
- Experience with cloud-based data persistence products, especially RDS PostgreSQL and PostgreSQL extensions such as pgvector
- Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations
Responsibilities
- Designs, configures, develops, tests, and supports informatics and data science solutions for a wide array of technical use cases
- Collaborate with cross-functional teams, including data scientists and software engineers to integrate AI solutions developed by other elements of the DoD community into Portfolio products when appropriate
- Optimize AI models for performance, scalability, and efficiency, leveraging cloud-based resources and distributed computing frameworks, specifically Apache Spark/Databricks. Ability to adapt code base to also run using GPU enabled Kubernetes clusters
- Stay updated on and contribute to the latest advancements in AI research, applying new findings to improve Search Portfolio products
- Manage the lifecycle of AI/ML components used in Portfolio products from research and development to deployment and optimization
- Applies analytical methodologies to diagnose data-related challenges, implement solutions, and evaluate performance
- Documents and presents requirements, design alternatives, and findings to team members and clients
- Ability to develop strategic, baselined, data modeling processes; ability to accurately determine cause-and-effect relationships
- Experience with integrated development environments, data integration, data visualization, data mining, and analysis tools
- Maintains and guides the development of common libraries and tools used by multiple teams
- Aids in formulating a strategy on how to achieve rapid prototyping
Preferred Qualifications
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No preferred qualifications provided.