About this role.
Oddball is seeking a hands-on Applied AI/Machine Learning Engineer to build, deploy, evaluate, and improve production AI capabilities for federal-space software products. The role covers conventional machine learning, deep learning, NLP, and GenAI systems such as LLM workflows, retrieval-augmented generation, and agents. The engineer will develop data and feature pipelines, assess model quality, bias, drift, reliability, cost, and latency, and collaborate closely with engineering, design, and product partners. This is a hybrid/remote role requiring candidates to be located in the DC, Maryland, or Virginia area and authorized to work in the United States.
Role DNA
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Job Complexity
4/5Pace & Pressure
4/5Autonomy Level
4/5Communication Load
4/5Salary analysis
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Core skills
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Sample interview questions
I would begin by converting the user problem into a measurable ML objective and selecting both offline and production metrics. After establishing a baseline, I would build a reproducible training and evaluation pipeline, deploy the model behind an observable service, and use post-launch quality, latency, reliability, and user-impact data to guide iterations.
I would evaluate retrieval and generation separately as well as end to end. Retrieval metrics would include recall and ranking quality against curated queries, while generation review would assess factual grounding, citation quality, task completion, safety, and consistency; production monitoring would track latency, cost, feedback, and failure patterns.
I first identify the minimum acceptable user experience and the business impact of errors or delays. I then compare baseline and candidate approaches using representative traffic, optimize the highest-impact bottleneck through techniques such as caching, smaller models, batching, or retrieval improvements, and document the chosen tradeoff with measurable service objectives.
I would track input-data distributions, prediction distributions, performance against delayed labels where available, error rates, latency, throughput, and infrastructure health. I would establish alert thresholds, maintain versioned data and model artifacts, investigate meaningful deviations, and use controlled rollback or retraining procedures when performance degrades.
I would facilitate discovery around users, workflows, constraints, and the cost of incorrect outcomes, then translate those findings into a narrow hypothesis and measurable experiment. I would communicate assumptions and limitations clearly, demonstrate prototypes early, and use stakeholder feedback plus evidence from evaluation to prioritize the next iteration.
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/Remote. Candidates must be located in the DMV area (DC, Maryland, Virginia) and able to participate with in-office collaboration.
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
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