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Freelance Machine Learning Compiler Engineer Jobs in Washington, DC

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 ...

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 ...

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and ...

Machine Learning Engineer

Washington, DC · On-site

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build, evaluate, and deploy advanced machine learning systems across a range of safety, security, and ...

Machine Learning Engineer

Arlington, VA · On-site

$77K - $176K/yr

R0245170 Machine Learning Engineer The Opportunity: As an experienced AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to ...

Machine Learning Engineer

Arlington, VA · On-site

$77K - $176K/yr

R0245828 Machine Learning Engineer The Opportunity: As an experience d AI and ML engineer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to ...

Job Summary : aisquared is a company focused on AI solutions, and they are seeking a highly skilled Machine Learning Engineer to join their core AI team. In this role, you will be responsible for ...

Showing results 21-40

Freelance Machine Learning Compiler Engineer information

See Washington, DC salary details

$16

$54

$149

How much do freelance machine learning compiler engineer jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for freelance machine learning compiler engineer in Washington, DC is $54.03, according to ZipRecruiter salary data. Most workers in this role earn between $27.50 and $69.95 per hour, depending on experience, location, and employer.

What is the difference between Freelance Machine Learning Compiler Engineer vs Freelance Software Developer?

AspectFreelance Machine Learning Compiler EngineerFreelance Software Developer
Required SkillsMachine learning frameworks, compiler optimization, programming (C++, Python)General programming, software design, various languages
Work EnvironmentProject-based, remote, often technical teams in AI/ML industryVaried industries, remote or on-site, broad application areas
Industry UsageAI/ML companies, research labs, tech firmsTech, finance, healthcare, startups, enterprise

Freelance Machine Learning Compiler Engineers focus on optimizing ML models for deployment, requiring specialized knowledge in ML frameworks and compiler technology. Freelance Software Developers have broader roles across various industries, working on diverse software projects. Both roles are in high demand but differ in technical focus and industry application.

What are the most commonly searched types of Machine Learning Compiler Engineer jobs in Washington, DC? The most popular types of Machine Learning Compiler Engineer jobs in Washington, DC are:
What are popular job titles related to Freelance Machine Learning Compiler Engineer jobs in Washington, DC? For Freelance Machine Learning Compiler Engineer jobs in Washington, DC, the most frequently searched job titles are:
What job categories do people searching Freelance Machine Learning Compiler Engineer jobs in Washington, DC look for? The top searched job categories for Freelance Machine Learning Compiler Engineer jobs in Washington, DC are:

Machine Learning Engineer

AI Squared

Washington, DC • On-site

Full-time

Re-posted 20 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.