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Machine Learning Engineer Jobs in Charleston, WV

Senior Applied AI Engineer

Charleston, WV ยท Remote

$113K - $149K/yr

Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Engineering, Data Science, or a related technical discipline (or equivalent professional ...

VP of Data and AI

Charleston, WV ยท Remote

$251K - $346K/yr

Required Experience * 10+ years of progressive experience in AI, machine learning, data science, data engineering, or a related field, with 5+ years in a senior leadership role (Sr Director, VP) ...

As a Prompt Engineer, you will be a key member of our AI development team, responsible for ... Familiarity with machine learning frameworks and libraries like TensorFlow, PyTorch, or Hugging ...

Senior AI/ML Platform Engineer

Charleston, WV ยท Remote

$143K - $197K/yr

Build services that expose machine learning and AI based products * Deploy and manage various ... Engineer, you will be employed by Lyra Health, Inc. The anticipated annual base salary range for ...

The platform is built on Databricks for machine learning and data science and on AWS for production ... This leader will manage experienced engineers and specialists with significant autonomy, help ...

... of hands-on machine learning / AI engineering experience * Proven experience architecting and delivering solutions across multiple technical domains, such as APIs and microservices, systems ...

Senior Data Scientist

Charleston, WV ยท Remote

$97K - $124K/yr

... other programming languages used for data science purposes * Ability to collaborate closely with business and technical leaders. * Ability to work with machine learning frameworks, such as ...

Data Engineer

Charleston, WV ยท Remote

$118K - $148K/yr

As a Data Engineer, you will play a critical role in shaping Gopuff's modern data platform. You'll ... and machine learning * Contribute to the architecture and maintenance of the Data Platform ...

Showing results 21-40

Machine Learning Engineer information

See Charleston, WV salary details

$30.6K

$125.1K

$188.1K

How much do machine learning engineer jobs pay per year?

As of Aug 6, 2026, the average yearly pay for machine learning engineer in Charleston, WV is $125,147.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,600.00 and $150,600.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Charleston, WV? The most popular types of Machine Learning Engineer jobs in Charleston, WV are:
What are popular job titles related to Machine Learning Engineer jobs in Charleston, WV? For Machine Learning Engineer jobs in Charleston, WV, the most frequently searched job titles are:
What cities near Charleston, WV are hiring for Machine Learning Engineer jobs? Cities near Charleston, WV with the most Machine Learning Engineer job openings:
Infographic showing various Machine Learning Engineer job openings in Charleston, WV as of July 2026, with employment types broken down into 91% Full Time, 6% Part Time, and 3% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $125,147 per year, or $60.2 per hour.

Senior Applied AI Engineer

LTS

Charleston, WV โ€ข Remote

$113K - $149K/yr

Full-time

Posted 4 days ago


Job description

Location: United States – Remote
Clearance: Ability to obtain and maintain a Public Trust

LTS is seeking a highly skilled Senior Applied AI Engineer to focus on continuously improving the intelligence behind the platform. You'll experiment with models, optimize retrieval strategies, refine agent reasoning, evaluate AI performance, and transform emerging AI capabilities into production-ready solutions.

The Agentic AI platform is designed to help engineers understand, analyze, and modernize one of the most consequential legacy software systems still operating today.

Our platform enables engineers to ask questions in plain English and receive explainable, verifiable answers traced directly back to decades of production source code. Rather than replacing engineers, we're building AI that accelerates engineering through transparency, traceability, and intelligent reasoning.

We're building an AI-native engineering platform supporting the modernization of mission-critical healthcare systems serving millions of Veterans nationwide.

The platform is designed for deployment across federal enterprise environments and is being engineered to align with FedRAMP security controls, Zero Trust principles, and federal compliance requirements.

The platform has executive sponsorship, committed users, and a customer investing in long-term modernization. Our engineering team is intentionally small giving every engineer meaningful ownership, and direct influence over product direction.

We don't simply build AI-powered software—we build software with AI. This is not another chatbot.

Using LLMs, autonomous agents, AI-assisted development, parallel workflows, and model-driven engineering is simply how we work.

What You'll Do:

Advance Applied AI Capabilities

  • Design, prototype, and implement production-ready AI capabilities that improve reasoning, accuracy, explainability, and developer productivity.
  • Evaluate emerging LLMs, multimodal models, agent frameworks, and AI techniques to identify opportunities for platform advancement.
  • Rapidly prototype new AI capabilities and transition successful experiments into production.

Optimize Agent Performance

  • Improve autonomous and multi-agent workflows through prompt engineering, reasoning optimization, memory strategies, tool selection, and context management.
  • Continuously refine Retrieval-Augmented Generation (RAG) pipelines, retrieval strategies, embeddings, reranking, and grounding techniques.
  • Improve AI response quality through experimentation, benchmarking, and iterative optimization.

Evaluate AI Systems

  • Develop evaluation frameworks that measure accuracy, groundedness, explainability, latency, and overall AI effectiveness.
  • Build benchmark datasets, automated evaluation pipelines, and performance metrics for production AI systems.
  • Analyze AI failures, hallucinations, retrieval gaps, and reasoning errors to drive continuous improvement.

Knowledge Engineer

  • Collaborate with software engineers to improve knowledge ingestion, document processing, semantic search, embeddings, and enterprise knowledge management.
  • Design approaches that maximize retrieval quality across large technical documentation and source code repositories.
  • Improve how AI agents discover, organize, and reason over enterprise knowledge.

Collaborate Across Engineer

  • Partner closely with AI architects, platform engineers, software engineers, and front-end engineers to improve the overall intelligence of the platform.
  • Share research findings, experimental results, and engineering recommendations with cross-functional teams.
  • Help establish best practices for experimentation, evaluation, and AI quality throughout the organization.

What We're Looking For:

  • Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Engineering, Data Science, or a related technical discipline (or equivalent professional experience).
  • 5+ years of software engineering, applied AI, machine learning, or AI systems development experience.
  • Demonstrated experience developing production AI applications powered by Large Language Models (LLMs).
  • Experience designing and optimizing Retrieval-Augmented Generation (RAG) systems.
  • Experience with prompt engineering, embeddings, semantic search, vector databases, and knowledge retrieval.
  • Experience evaluating AI model performance and implementing experimentation frameworks.
  • Strong programming skills in Python and experience with modern software engineering practices.
  • Experience with AI frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.
  • Experience with using AI coding assistants as part of your daily workflow.
  • Familiarity with REST APIs, cloud-native applications, and distributed software systems.
  • Strong analytical, problem-solving, and communication skills.
  • Intellect and curiosity for AI systems and how they behave.
  • Deep passion for experimenting with new AI techniques.
  • Background in evaluation, explainability, and continuous improvement.
  • Proven success with ownership of difficult technical challenges and collaboration across disciplines.

Nice to Have:

  • Experience optimizing autonomous or multi-agent AI systems.
  • Experience implementing automated AI evaluation frameworks.
  • Experience with OpenAI, Azure OpenAI, Anthropic Claude, Google Vertex AI, AWS Bedrock, or open-source LLMs.
  • Experience with vector databases including Pinecone, Weaviate, Qdrant, Milvus, or Azure AI Search.
  • Experience with Responsible AI, AI governance, safety, and explainability.
  • Familiarity with software engineering tools, code intelligence platforms, or developer productivity solutions.
  • Experience supporting healthcare, Federal Government, or other highly regulated environments.
  • Experience using AI coding assistants and autonomous agents as part of daily software development.

What's In It for You?

  • The Opportunity to support high-visibility federal missions
  • A culture that values innovation, growth, and collaboration
  • Access to cutting-edge tools and technologies
  • Comprehensive benefits for you and your family
  • A career path that rewards ambition and performance

If you're ready to push boundaries, sharpen your skills, and join a team that is passionate about building what's next, we'd love to meet you. Apply today and let's build a future together!

LTS shares salary ranges to promote transparency. Compensation ranges are provided for informational purposes, and final compensation may vary based on experience, skills, location, and role requirements.

LTS is committed to offering eligible employees comprehensive benefits that will provide them with options intended to meet their needs and the needs of their family.