Applied AI/ML Engineer

Oddball•Washington, DC

2h•Hybrid

About The Position

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.

Requirements

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
Applicants must be authorized to work in the United States.
In alignment with federal contract requirements, certain roles may also require U.S. citizenship and the ability to obtain and maintain a federal background investigation and/or a security clearance.

Nice To Haves

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

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