Applied AI/ML Engineer

Job Overview

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.

Job Description

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.

Key Responsibilities

• We’re looking for an Applied AI / Machine Learning Engineer to design, build, and deploy practical AI-powered solutions that solve real-world problems.
• 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.

Required Skills and Qualifications

• 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.
• Be advised, actual offer details are determined by job category, job location, and candidate skill level.

Benefits and Perks

• 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.
• 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.

Work Location and Schedule

This role is listed as Remote USA with location information shown as Remote USA. The employment type is Full Time.

About the Company

Oddball is the organization connected with this listing. USA Jobs Today displays this opportunity for job discovery only, so applicants should verify company details, application instructions, and eligibility on the official employer website.