About this role
Federal systems often struggle to leverage modern data and complex user needs, creating a demand for practical automation that Oddball aims to address. Bringing quality software to the federal space allows the team to improve the daily lives of millions of people through targeted technology improvements.
Building out these capabilities requires designing, building, and deploying practical artificial intelligence solutions that solve real-world problems. Engineers in this position handle everything from initial experimentation and prototyping through final deployment, evaluation, and iteration of machine learning models.
Success in the group relies on a balance between technical rigor and real-world constraints while turning ambiguous problems into scalable, reliable features. Candidates who enjoy shipping code quickly, learning on the job, and collaborating closely with cross-functional partners find a strong fit here.
What you will do
- 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, natural language processing, or anomaly detection.
- Develop and integrate generative artificial intelligence solutions including language model workflows and retrieval-augmented generation.
- Translate business and user needs into concrete machine learning 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 while iterating based on results.
- Collaborate with software engineers to integrate models into application programming interfaces, services, and user-facing applications.
What you need
- Demonstrate a strong foundation in machine learning concepts including model selection, training, validation, and evaluation.
- Bring previous experience building and deploying machine learning models in real-world applications.
- Show proficiency in Python and common machine learning libraries such as PyTorch, TensorFlow, and scikit-learn.
- Possess experience working with large language models, embeddings, and prompt-driven systems.
- Maintain familiarity with data processing tools and workflows including Pandas, SQL, or Spark.
- Understand software engineering best practices such as version control, testing, and code reviews.
- Communicate effectively and work comfortably within cross-functional teams.
Nice to have
- 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
Benefits
- Fully remote
- Annual stipend
- Comprehensive Benefits Package
- Company Match 401(k) plan
- Flexible PTO, Paid Holidays
Skills and tech stack
Questions candidates ask
What is the work location policy for this position?
The arrangement is hybrid or remote, but candidates must be located in the DMV area covering DC, Maryland, and Virginia to participate in occasional in-office collaboration.
What is the compensation range for the applied artificial intelligence role?
The United States wage range for the position spans from $150,000 to $200,000. Actual offer details are determined by job category, location, and candidate skill level.
Are there citizenship or security clearance prerequisites?
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 or security clearance.