Full Time

Applied AI/Machine Learning Engineer - Oddball - McLean, VA

Oddball

McLean, VA
150K–200K a year
Posted 16 days ago

Oddball believes that the best products are built when companies understand and value the things they are working on. We value learning and growth and the ability to make a big impact at a small company. We believe that we can make big changes happen and improve the daily lives of millions of people by bringing quality software to the federal space.

We’re looking for an Applied AI / Machine Learning Engineer to design, build, and deploy practical AI-powered solutions that solve real-world problems. This role focuses on applying modern ML and GenAI techniques in production systems — from experimentation and prototyping through deployment, evaluation, and iteration. You’ll work closely with engineers, designers, and product stakeholders to turn ambiguous problems into scalable, reliable AI-driven capabilities.

This is a hands-on engineering role for someone who enjoys shipping, learning quickly, and balancing technical rigor with real-world constraints.

What you'll be doing:
• Design, develop, and deploy machine learning and AI-powered features into production systems
• Apply supervised, unsupervised, and deep learning techniques to structured and unstructured data
• Build and evaluate models for tasks such as classification, ranking, prediction, NLP, or anomaly detection
• Develop and integrate GenAI solutions (e.g., LLM-based workflows, retrieval-augmented generation, agents)
• Translate business and user needs into ML problem statements, metrics, and experiments
• Implement data pipelines and feature engineering workflows to support model training and inference
• Evaluate model performance, bias, drift, and reliability; iterate based on results
• Collaborate with software engineers to integrate models into APIs, services, and user-facing applications
• Contribute to architecture decisions around model serving, scalability, and cost optimization
• Document approaches, assumptions, and tradeoffs to support maintainability and knowledge sharing

What you’ll bring: