Applied Machine Learning Scientist

Workiva

. Contribute to the development, testing, and deployment of AI and ML solutions .

Posted 9/24/2026full-timeRemote • United StatesJuniorMid-Level💰 $105,000 - $185,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates proficiency in Python and foundational knowledge of machine learning, including model development, evaluation, and data preparation. Capable of collaborating with cross-functional teams to build and deploy AI solutions while maintaining high standards of code quality and documentation.

Highest-signal resume keywords

Python ProgrammingMachine Learning Model DevelopmentData Preparation and Feature EngineeringMLOps PracticesCollaboration and Communication Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData ScienceModel EvaluationStatistical ModelingFeature EngineeringData AnalysisSoftware DevelopmentExperiment DocumentationError AnalysisPerformance Metrics

Soft Skills

Analytical SkillsCuriosityCollaboration SkillsCommunication Skills

Tools & Technologies

PyTorchTensorFlowScikit-learnHugging FacePandasNumPySciPySQLAPIsCI/CD

Industry Keywords

AI SolutionsGenerative AILLM EvaluationModel-Serving ConceptsData PipelinesCloud PlatformsSoftware Engineering Practices

Tech Stack

Tools & technologies

CloudNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Contribute to the development, testing, and deployment of AI and ML solutions
Build, evaluate, and iterate on machine learning models, experiments, prototypes, and data-driven features
Assist with data preparation, feature development, model training, model evaluation, error analysis, and experiment documentation
Write clean, efficient, testable, and maintainable code for ML and AI applications
Partner with applied scientists, engineers, product managers, and stakeholders to translate requirements into technical tasks
Participate in experimentation and measurement, including defining hypotheses, selecting evaluation metrics, and interpreting results
Develop and maintain reusable tools, libraries, datasets, and workflows supporting the ML development lifecycle
Support integration of models and AI capabilities into scalable product and platform solutions
Learn and apply MLOps, cloud, observability, testing, and operational practices
Assist with monitoring and improving ML systems for quality, performance, reliability, and efficiency
Document technical approaches, experiment results, model limitations, and implementation decisions
Participate in code reviews, design discussions, team planning, and knowledge-sharing activities
Stay current with machine learning, generative AI, LLMs, statistical modeling, and applied AI techniques
Contribute ideas improving experimentation culture, engineering practices, and product outcomes

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field; equivalent practical experience may be considered
0–2 years of hands-on experience through professional work, internships, research, coursework, or projects in machine learning, AI, data science, software engineering, or a related technical area
Proficiency in Python, R, or a similar language for data analysis, model development, or software development
Foundational knowledge of machine learning, including data preparation, feature engineering, training, validation, model evaluation, and common performance metrics
Familiarity with ML and data-science tools such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, pandas, NumPy, SciPy, or Statsmodels
Exposure to generative AI, including prompt engineering, retrieval-augmented generation, embeddings, LLM evaluation, fine-tuning, or agentic workflows
Familiarity with SQL, APIs, data pipelines, model-serving concepts, cloud platforms, or MLOps practices
Exposure to software-engineering practices such as Git, code review, testing, CI/CD, or containers
Strong analytical, communication, and collaboration skills, with curiosity and a willingness to learn in a fast-moving applied AI environment
Interest in building responsible, reliable, and measurable AI solutions that address customer needs
Minimal travel required, up to 10%
Reliable internet access is required for any period of time working remotely and not in a Workiva office
Candidates must be authorized to work in the U.S. on a permanent basis

Benefits

Comp & perks

A discretionary bonus typically paid annually
Restricted Stock Units granted at time of hire
401(k) match
Comprehensive employee benefits package
Flexible work location: office or remotely from any location within the country of employment
Reasonable accommodations for applicants with disabilities