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Machine Learning Infrastructure Engineer Jobs in Washington

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Machine Learning Engineer

Reston, VA

Machine Learning Engineer Location: Fort Meade, MD Required Clearance : TS/SCI w/ Full-Scope Poly ... Develop and maintain machine learning pipelines and infrastructure. * Stay current with the latest ...

10a Labs

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 ... ML infrastructure, workflow orchestration, storage, and database services. * Familiarity or ...

10a Labs

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 ... ML infrastructure, workflow orchestration, storage, and database services. * Familiarity or ...

Ametek

Machine Learning Engineer

Herndon, VA · Hybrid

We are seeking an earlycareer Machine Learning Engineer who is excited to grow rapidly by building ... Experience with cloud infrastructure and managing resources in the cloud. * Master's degree in a ...

Beyond SOF

Engineer, Machine Learning

Washington, DC

The Machine Learning Engineer is responsible for developing and implementing machine learning models and algorithms to solve complex problems. Main Responsibilities and Duties: Develop and implement ...

Molg

Infrastructure Engineer

Sterling, VA · On-site

$106K - $139K/yr

As an Infrastructure Engineer, you will: * Own the design, deployment, and operation of Molg ... in learning how to use our various pieces of equipment and machinery is taught and can gain the ...

Ametek

Machine Learning Engineer

Herndon, VA · On-site

We are seeking an early-career Machine Learning Engineer who is excited to grow rapidly by building ... Experience with cloud infrastructure and managing resources in the cloud. * Master's degree in a ...

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Machine Learning Infrastructure Engineer information

What are some common challenges faced by Machine Learning Infrastructure Engineers, and how can these be addressed on the job?

Machine Learning Infrastructure Engineers often face challenges such as ensuring infrastructure scalability, managing resource allocation, and maintaining system reliability while supporting rapid experimentation by data science teams. Balancing the needs for flexibility in research environments with production-grade stability requires a deep understanding of both engineering best practices and the unique requirements of machine learning workflows. Collaboration with data scientists, clear communication about infrastructure capabilities, and staying current with fast-evolving technologies are key strategies for success. Most companies encourage ongoing learning and provide opportunities to contribute to architecture decisions, which makes this a rewarding environment for problem-solvers and innovators.

What are the key skills and qualifications needed to thrive in the Machine Learning Infrastructure Engineer position, and why are they important?

To thrive as a Machine Learning Infrastructure Engineer, you need a strong background in computer science, cloud computing, distributed systems, and experience with machine learning frameworks, often supported by a degree in a related field. Familiarity with tools such as Docker, Kubernetes, Terraform, as well as cloud platforms like AWS, GCP, or Azure, and certifications in cloud or DevOps technologies are highly valued. Strong problem-solving abilities, effective communication, and collaboration skills help engineers work seamlessly with data scientists and cross-functional teams. These skills are essential to design, implement, and maintain robust, scalable infrastructure that enables efficient machine learning development and deployment.

What is a Machine Learning Infrastructure Engineer job?

A Machine Learning Infrastructure Engineer designs, builds, and maintains the systems that support the development and deployment of machine learning models. This includes managing data pipelines, optimizing model training and inference, and ensuring scalability and reliability in production environments. They work closely with data scientists, ML engineers, and DevOps teams to create efficient workflows and infrastructure. Key technologies often include cloud platforms, containerization, orchestration tools, and distributed computing frameworks.

What are popular job titles related to Machine Learning Infrastructure Engineer jobs in Washington? For Machine Learning Infrastructure Engineer jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Machine Learning Infrastructure Engineer jobs in Washington look for? The top searched job categories for Machine Learning Infrastructure Engineer jobs in Washington are:
What cities in Washington are hiring for Machine Learning Infrastructure Engineer jobs? Cities in Washington with the most Machine Learning Infrastructure Engineer job openings:
Machine Learning Infrastructure Engineer jobs near you

Machine Learning Engineer

Full Scope

Reston, VA

Other

Posted 9 days ago

Job description

Job Title:Machine Learning Engineer
Location:Fort Meade, MD
Required Clearance: TS/SCI w/ Full-Scope Poly
Salary:Competitive
We are seeking a highly skilled and motivated Machine Learning Engineer to join our dynamic team. The ideal candidate will have a strong background in machine learning, data science, and software engineering. You will work closely with data scientists, engineers, and product managers to design, develop, and deploy machine learning models and solutions that drive business value.
Key Responsibilities:
  • Design, develop, and implement machine learning models and algorithms to solve real-world problems.
  • Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
  • Conduct data analysis and preprocessing to ensure high-quality data for model training.
  • Optimize and fine-tune models for performance, accuracy, and scalability.
  • Deploy machine learning models into production and monitor their performance.
  • Develop and maintain machine learning pipelines and infrastructure.
  • Stay current with the latest research and advancements in machine learning and AI.
  • Participate in code reviews, team meetings, and contribute to a collaborative development environment.
  • Document processes, models, and findings comprehensively.
Qualifications:
  • Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field. Ph.D. is a plus.
  • Proven experience as a Machine Learning Engineer or in a similar role.
  • Strong proficiency in programming languages such as Python, R, or Java.
  • Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
  • Solid understanding of machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning.
  • Experience with data processing tools like Pandas, NumPy, and data visualization tools such as Matplotlib or Seaborn.
  • Familiarity with cloud platforms like AWS, Google Cloud, or Azure for model deployment and scaling.
  • Strong problem-solving skills and the ability to think critically and analytically.
  • Excellent communication and teamwork skills.
Preferred Qualifications:
  • Experience with natural language processing (NLP) and computer vision.
  • Familiarity with big data technologies such as Hadoop, Spark, or Kafka.
  • Knowledge of software development best practices and version control systems like Git.
  • Experience with containerization tools like Docker and orchestration tools like Kubernetes.
  • Previous experience in a fast-paced, startup environment.

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