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Rf Machine Learning Engineer Jobs (NOW HIRING)

Machine Learning Engineer Washington, DC (Hybrid) About the Role: We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying ...

Job Title Machine Learning Engineer Location Remote Rate $48/hr on W2 Must Haves: Neaural networks NLP Python AZURE Pytorch or tensorflow Machine Learning Engineer / AI Engineer Role Role Overview ...

About the role: We're looking for an early career Machine Learning Engineer to join our team. In this role you will build and deploy state of the art machine learning models to solve complex ...

Machine Learning Engineer We're looking for a talented and motivated Machine Learning Engineer to join our team and help develop cutting-edge AI solutions. In this role, you'll have the opportunity ...

We are looking for a Machine Learning Engineer to design, build, and deploy machine learning systems that improve the calibration, control, and operation of quantum processors. In this role, you will ...

Machine Learning Engineer

Plano, TX · On-site

$53.50 - $71/hr

As a Performance Assurance Machine Learning Engineer, you will work under the coaching of Senior ... Collaborate with RF engineers, network engineers, data scientists, platform engineers, product ...

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Rf Machine Learning Engineer information

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$37K

$117.7K

$183K

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

As of Sep 11, 2026, the average yearly pay for rf machine learning engineer in the United States is $117,680.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,500.00 and $139,000.00 per year, depending on experience, location, and employer.

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

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Infographic showing various Rf Machine Learning Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $117,680 per year, or $56.6 per hour.

Machine Learning Engineer

Washington, DC • On-site

Full-time

Re-posted 22 days ago


Job description

Machine Learning Engineer
Washington, DC (Hybrid)
About the Role:
We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying, maintaining, and monitoring the AI/ML systems that power our platform. You will work closely with data scientists, data engineers, and product teams to ensure scalable, reliable, and production-grade AI solutions. You'll play a critical role in operationalizing large language models (LLMs) and other ML systems, ensuring they run efficiently, securely, and with robust monitoring in place.
Key Responsibilities:
  • Design, implement, and maintain ML deployment pipelines for scalable production systems.
  • Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
  • Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
  • Partner with data scientists to transition models from research/prototype into production-ready deployments.
  • Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
  • Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
  • Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
  • Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.

Qualifications:
  • 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
  • Proven experience deploying and maintaining machine learning models in production at scale.
  • Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
  • Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
  • Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
  • Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
  • Strong understanding of MLOps best practices, monitoring, and automation.
  • Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
  • Strong communication and collaboration skills across technical and non-technical teams.