Applied AI/ML Engineer

Summary

Oddball is a software company focused on delivering quality products for the federal space. The Applied AI/ML Engineer will design, build, deploy, evaluate, and iterate on practical machine learning and generative AI solutions, collaborating with engineering, design, and product stakeholders to create scalable and reliable AI capabilities.

Responsibilities

• 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
• Performs other related duties as assigned

Skills

• Strong foundation in machine learning concepts, including model selection, training, validation, and evaluation
• Experience building and deploying ML models in real-world applications
• Proficiency in Python and common ML libraries (e.g., PyTorch, TensorFlow, scikit-learn)
• Experience working with large language models, embeddings, and prompt-driven systems
• Familiarity with data processing tools and workflows (e.g., Pandas, SQL, Spark, or similar)
• Understanding of software engineering best practices (version control, testing, code reviews)
• Ability to reason about tradeoffs between accuracy, latency, cost, and maintainability
• Strong communication skills and comfort working in cross-functional teams
• Experience deploying models to cloud platforms and managing inference at scale
• Familiarity with MLOps practices such as model monitoring, CI/CD for ML, and experiment tracking
• Experience contributing to architectural discussions or technical strategy
• Candidates must be located in the DMV area (DC, Maryland, Virginia) and able to participate with in-office collaboration
• Applicants must be authorized to work in the United States
• Experience working in innovation, R&D, labs, or exploratory engineering teams

Qualifications

Must Haves

• Strong foundation in machine learning concepts, including model selection, training, validation, and evaluation
• Experience building and deploying ML models in real-world applications
• Proficiency in Python and common ML libraries (e.g., PyTorch, TensorFlow, scikit-learn)
• Experience working with large language models, embeddings, and prompt-driven systems
• Familiarity with data processing tools and workflows (e.g., Pandas, SQL, Spark, or similar)
• Understanding of software engineering best practices (version control, testing, code reviews)
• Ability to reason about tradeoffs between accuracy, latency, cost, and maintainability
• Strong communication skills and comfort working in cross-functional teams
• Experience deploying models to cloud platforms and managing inference at scale
• Familiarity with MLOps practices such as model monitoring, CI/CD for ML, and experiment tracking
• Experience contributing to architectural discussions or technical strategy
• Candidates must be located in the DMV area (DC, Maryland, Virginia) and able to participate with in-office collaboration
• Applicants must be authorized to work in the United States

Nice to Haves

• Experience working in innovation, R&D, labs, or exploratory engineering teams

Benefits

• Fully remote
• Annual stipend
• Comprehensive Benefits Package
• Company Match 401(k) plan
• Flexible PTO
• Paid Holidays