Job description

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: 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 Performs other related duties as assigned Bonus if you 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 Location: Hybrid — DMV area required (DC, Maryland, Virginia). Candidates must be able to commute to the office Tuesday, Wednesday, and Thursday. Requirements: 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. Benefits: Fully remote Annual stipend Comprehensive Benefits Package Company Match 401(k) plan Flexible PTO, Paid Holidays Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities: Oddball is an Equal Opportunity Employer and does not discriminate against applicants based on race, religion, color, disability, medical condition, legally protected genetic information, national origin, gender, sexual orientation, marital status, gender identity or expression, sex (including pregnancy, childbirth or related medical conditions), age, veteran status or other legally protected characteristics. Any applicant with a mental or physical disability who requires an accommodation during the application process should contact an Oddball HR representative to request such an accommodation by emailing hr@oddball.io The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60-1.35(c) Compensation: At Oddball, it’s important each employee is compensated competitively and fairly. In alignment with state legal requirements. A range for the included position is listed below. Be advised, actual offer details are determined by job category, job location, and candidate skill level. United States Wage Range: $150,000 – $200,000

Requirements

Must-haves

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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlllmpythonsqlci/cd

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

401(K)

Pto