Machine Learning Engineer Associate Jobs in Tennessee

Hot Job

• Job Identification 340943
• Job Category Product and Research
• Posting Date 07/23/2026, 11:30 PM
• Job Type Regular Employee
• Does this position require a security clearance? No
• Years 6 to 10+ years
• Applicants are required to read, write, and speak the following languages English

Implements machine learning (ML) models for production. Ensures the readiness of machine learning models for deployment in production. Automates machine learning workflows. Creates infrastructure and frameworks to monitor the performance of machine learning models in deployment. Evaluates potential data quality, security, and/or privacy issues and their impacts on modeling. Provides troubleshooting and debugging support. Addresses issues in machine learning infrastructure and workflows. Collaborates with stakeholders to integrate machine learning models into new or extant systems. Develops, maintains, and refines tools, platforms, and services for internal use. Develops efficient, bug-free code from scratch. Maintains familiarity with current developments in the machine learning field and integrates knowledge into model development.

KeyResponsibilities

MachineLearning and Data Modeling – Model Productionization:

• Utilizesmachine learning (ML) and software development knowledge to implement ML modelsfor production.
• Engagesin transforming machine learning prototypes into production-ready models.
• Collaborateswith multiple stakeholders, such as Development Leads, Product Management,Operations, and Release Management, to make, adopt, and communicate technicaldecisions, and shape the development and delivery of software.

ModelDevelopment and Deployment – Model Deployment:

• EnsuresML model readiness for deployment by scaling models, cleaning model code, andensuring production quality standards are met.
• Automatesmachine learning workflows, from data extraction, transformation, and loading(ETL) to model deployment and monitoring, to establish the continuousintegration and continuous delivery of machine learning solutions.

ModelDevelopment and Deployment – Model Performance:

• Createsinfrastructure and frameworks to monitor the performance and alignment withdesign criteria of trained models and/or systems.
• Proactivelymonitors the performance of deployed models and troubleshoots independently orin collaboration with Data Science.
• Developsnovel metrics that provide analytical insights to non-technical stakeholders onhow well machine learning models are operating.

ModelDevelopment and Deployment – Data Quality:

• Evaluatespotential issues related to data quality (e.g., bias, fairness), data security,and data privacy, and minimizes their impacts on data analyses and modeling.
• Engagesin tasks such as data cleaning, preprocessing, and feature identification toprepare for and enable model training.

InternalCollaborations and Impacts – Model Integration and Operation:

• Collaborateswith multiple stakeholders (e.g., data scientists, software developers) tointegrate ML models into new or existing systems.
• Maintainsthe partnership between model development and operations, ensuring smoothdeployment and continuous improvement of ML models.
• Understandssoperational considerations of model deployment (e.g., performance, scalability,stability, maintenance).
• Providesexpert troubleshooting and debugging support, addresses issues in machinelearning infrastructure and workflow, and creates robust solutions to preventfuture problems.

InternalCollaborations and Impacts – Tool Development:

• Develops,maintains, and refines tools, platforms, environments, and services forinternal use.

InternalCollaborations and Impacts – Coding and Documentation:

• Developsefficient, bug-free, medium-complexity code from scratch, and properlymaintains and organizes the existing codebase.
• Implementsbest practices for version control, code review, and code delivery/deployment.
• Buildsand maintains professional documentation for technical processes(experimentation, data collection and analyses, model building).
• Testsand reviews code for bugs.

MachineLearning Expertise:

• Maintainsfamiliarity with current developments in the machine learning field andintegrates knowledge into model development.
• Maintainsfamiliarity with the usage and development of third-party machine learningframeworks, packages, and libraries (e.g., PyTorch, TensorFlow, Keras) tocontinuously evaluate their performance and scalability, and integrate theminto production environments.

CoreResponsibilities

Planning& Execution:

• Managesand coordinates moderately complex tasks, monitoring timelines and deliverablesto ensure timely completion and adherence to requirements for a moderatelysized project or initiative.
• Efficientlydelegates, monitors, and prioritizes work across multiple projects, providingtechnical oversight and adjusting plans to address shifts in resources ortimelines.
• Collaboratesacross the organization to align on expectations and achieve shared objectives.
• Leveragesunderstanding of business leaders, stakeholders, and/or customers to ensureproposed solutions meet their needs.
• Supportsinclusivity by actively seeking and listening to diverse perspectives, ensuringothers feel heard and respected.

ProblemSolving:

• Identifiesand addresses moderately complex issues by analyzing a wide range of dataand/or information to identify solutions in accordance with standard practices.
• Proactivelyescalates unresolved or critical issues with a thorough assessment and suggestspotential solutions.
• Reviews,contributes to, and documents problem solving strategies.
• Pursueslearning opportunities to expand knowledge and skills and/or tools in new areasand stays abreast of the latest industry trends and best practices.
• Proactivelyseeks and leverages ongoing feedback and training to improve skills.
• Coachesand mentors junior team members, fostering continuous learning and knowledgesharing within and across teams.
• Developsideas, recommends updates, and/or collaborates on the implementation of processimprovements to increase the efficiency and effectiveness of processes,protocols, and workflows across teams, and evaluates the impact on keystakeholders.
• Solicitsfeedback from others on ideas for alternative approaches and methods forcontinued improvement.

Performanceand Development:

• Contributesto the talent development pipeline by participating in candidate interviews,assessing candidates, and providing hiring recommendations.

Disclaimer:

Certain U.S. based or U.S. customer or client-facing roles may be required to comply with applicable requirements, such as immunization/occupational health mandates, and/or drug testing requirements.

Range and benefit information provided in this posting are specific to the stated locations only

US: Hiring Range in USD from: $126,200 to