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Machine Learning Engineer Llm Jobs (NOW HIRING)

Compunnel

Senior Machine Learning Engineer

Raleigh, NC · On-site

$101K - $139K/yr

Job Summary: We are seeking a Senior Machine Learning Engineer to help lead the design and ... LLM, and Retrieval-Augmented Generation (RAG) applications for production environments. • ...

Northern Base

Machine Learning Engineer

Sunrise, FL · On-site

$90K - $110K/yr

Role - Machine Learning Engineer Experience Required -8+ Years We are seeking a Machine Learning ... Implement short-term and long-term memory strategies for LLM-based systems. * Optimize prompts ...

TEKsystems

Machine Learning Engineer

Raleigh, NC · On-site

$75 - $85/hr

Develop model evaluation frameworks for both traditional machine learning and LLM-based systems ... Strong software engineering fundamentals including testing, CI/CD, observability, and scalable ...

Nace AI

Machine Learning Engineer

Palo Alto, CA · On-site

Preferred Qualifications: * 5+ years of industry experience in machine learning engineering, with a track record of shipping LLM-based systems at scale. * MS/PhD in CS or related technical field.

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Machine Learning Engineer Llm information

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$128.8K

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How much do machine learning engineer llm jobs pay per year?

As of Sep 8, 2026, the average yearly pay for machine learning engineer llm in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What is a machine learning engineer LLM?

Machine Learning Engineers (LLM) are professionals who design, build, and deploy large language models (LLMs) such as GPT or BERT. They combine software engineering skills with a deep understanding of machine learning algorithms to develop systems that can process and generate human-like text. Their responsibilities often include data preprocessing, model training, fine-tuning, evaluation, and integrating these models into applications. They also work to optimize performance, ensure scalability, and address ethical considerations related to AI language models.

What are common challenges machine learning engineers face when working with large language models (LLMs) in a production environment?

Machine Learning Engineers working with LLMs often encounter challenges such as optimizing model performance while managing resource constraints like memory and compute power. Additionally, ensuring data privacy and compliance can be complex due to the vast amounts of training data involved. Another common challenge is deploying and monitoring LLMs to maintain accuracy and minimize bias, requiring close collaboration with data scientists, DevOps, and product teams. Regularly updating models to reflect new data and user feedback is also crucial for maintaining relevance and performance in real-world applications.

What are the key skills and qualifications needed to thrive as a machine learning engineer LLM?

To thrive as a Machine Learning Engineer specializing in large language models (LLMs), you need a strong background in computer science, mathematics, and deep learning, typically supported by a relevant degree and experience with NLP techniques. Familiarity with frameworks like TensorFlow, PyTorch, Hugging Face Transformers, and experience working with large-scale data sets and distributed systems are essential, along with knowledge of cloud platforms such as AWS or GCP. Strong problem-solving, collaboration, and communication skills help you translate complex research into practical applications and work effectively with cross-functional teams. These combined skills ensure the ability to develop, fine-tune, and deploy LLMs that deliver real-world value while staying at the forefront of AI advancements.

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

AspectMachine Learning Engineer LlmData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related; experience with ML frameworksBachelor's or Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops, tests, and deploys ML models, often in AI-focused teamsAnalyzes data, builds models, and provides insights for decision-making
Industry UsageUsed in AI product development, NLP, LLMs, and automationApplied across finance, healthcare, marketing, and research

While both roles require strong technical skills and knowledge of machine learning, Machine Learning Engineer Llm focuses on developing and deploying large language models, especially in AI applications. Data Scientists analyze data and build models for insights. The roles often overlap but differ mainly in their focus on deployment versus analysis.

What are popular job titles related to Machine Learning Engineer Llm jobs?

For Machine Learning Engineer Llm jobs, the most frequently searched job titles are:

Infographic showing various Machine Learning Engineer Llm job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer - LLM / MLOps

Reston, VA • On-site

HRC Global Services
See more jobs at HRC Global Servicesexternal

Full-time

Re-posted 24 days ago

Job description

Machine Learning Engineer – LLM / MLOps

Job Title: Machine Learning Engineer – LLM & MLOps
Location: Remote (U.S.)
Employment Type: Full-Time

About the Opportunity:
An exciting role for an ML Engineer to build scalable ML systems, deploy models, and work with cutting-edge AI technologies including LLMs and RAG architectures.

Key Responsibilities:

  • Build, train, and deploy ML models at scale
  • Develop reusable pipelines using Databricks and MLflow
  • Implement CI/CD workflows for ML deployment
  • Work with LLMs, RAG, and AI agent frameworks
  • Monitor model performance, drift, and retraining cycles

Required Skills:

  • 5+ years of ML Engineering experience
  • Strong Python programming and ML frameworks (PyTorch, TensorFlow, Scikit-learn)
  • Hands-on experience with Databricks, MLflow, PySpark
  • Experience with AWS (S3, SageMaker, Lambda, etc.)
  • Strong understanding of MLOps and model lifecycle

Preferred:

  • Experience building AI-driven applications (Streamlit, Gradio)
  • Strong system design and data pipeline experience
  • Business understanding of AI applications

Clearance: Public Trust (or eligible)

Hashtags:
#MLEngineer #MachineLearning #MLOps #LLM #AWS #Databricks #PySpark #AIEngineering #RemoteJobs #HiringNow