1

Senior Machine Learning Ops Engineer Jobs in Virginia

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... This position requires mid to senior level of experience, a passion for mission support, and a ...

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... This position requires mid to senior level of experience, a passion for mission support, and a ...

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... This position requires mid to senior level of experience, a passion for mission support, and a ...

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... This position requires mid to senior level of experience, a passion for mission support, and a ...

next page

Showing results 1-20

Senior Machine Learning Ops Engineer information

What are the key skills and qualifications needed to thrive as a Senior Machine Learning Ops Engineer, and why are they important?

To thrive as a Senior Machine Learning Ops Engineer, you need expertise in machine learning, software engineering, cloud platforms, and experience with CI/CD pipelines, often supported by a computer science degree or equivalent experience. Proficiency with tools like Docker, Kubernetes, TensorFlow, PyTorch, and cloud services such as AWS, GCP, or Azure is typically required, along with familiarity with MLOps frameworks. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and manage complex ML model deployments. These skills are essential to ensure reliable, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges faced by Senior Machine Learning Ops Engineers when deploying models to production?

Senior Machine Learning Ops Engineers often encounter challenges such as ensuring model reproducibility, managing model versioning, and automating deployment pipelines for scalability. Another key challenge is monitoring model performance and data drift in production, which requires robust logging and alerting systems. Collaborating closely with data scientists, software engineers, and IT teams is essential to address these challenges and maintain a stable, efficient ML infrastructure.

What are Senior Machine Learning Ops Engineers?

Senior Machine Learning Ops (MLOps) Engineers are experienced professionals who design, build, and maintain the infrastructure and tools needed to deploy, monitor, and scale machine learning models in production environments. They work at the intersection of data science, software engineering, and DevOps to ensure ML models are robust, reliable, and secure. Their responsibilities often include automating model training pipelines, managing cloud resources, implementing CI/CD for ML, and ensuring model reproducibility. Senior MLOps Engineers also mentor junior staff and help define best practices for the organization’s ML workflow.

What is the difference between Senior Machine Learning Ops Engineer vs Data Engineer?

AspectSenior Machine Learning Ops EngineerData Engineer
CredentialsExperience with ML frameworks, cloud platforms, scripting, and DevOps toolsStrong SQL, ETL, database, and programming skills, often with cloud experience
Work EnvironmentFocus on deploying, monitoring, and maintaining ML models in productionDesigning and building data pipelines and infrastructure for data processing
Industry UsageCommon in AI/ML-focused companies, tech firms, and data-driven organizationsWidespread across industries for data management and analytics

While both roles involve working with data and cloud platforms, the Senior Machine Learning Ops Engineer specializes in deploying and maintaining machine learning models, whereas the Data Engineer focuses on building data pipelines and infrastructure. Understanding these distinctions helps in choosing the right career path or job search focus.

What are the most commonly searched types of Machine Learning Ops Engineer jobs in Virginia? The most popular types of Machine Learning Ops Engineer jobs in Virginia are:
What job categories do people searching Senior Machine Learning Ops Engineer jobs in Virginia look for? The top searched job categories for Senior Machine Learning Ops Engineer jobs in Virginia are:
What cities in Virginia are hiring for Senior Machine Learning Ops Engineer jobs? Cities in Virginia with the most Senior Machine Learning Ops Engineer job openings:
Senior Machine Learning Research Scientist - Secure AI Lab

Senior Machine Learning Research Scientist - Secure AI Lab

Software Engineering Institute

Arlington, VA • On-site

$113.50K - $144.60K/yr

Other

This job post has expired today. Applications are no longer accepted.


Job description

Senior Machine Learning Research Scientist

At the SEI AI Division, we conduct research in applied artificial intelligence and the engineering questions related to the practical design and implementation of AI technologies and systems. We currently lead a community-wide movement to mature the discipline of AI Engineering for Defense and National Security.

As our government customers adopt AI and machine learning to provide leap-ahead mission capabilities, we:

  • build real-world, mission-scale AI capabilities through solving practical engineering problems
  • discover and define the processes, practices, and tools to support operationalizing AI for robust, secure, scalable, and human-centered mission capabilities
  • prepare our customers to be ready for the unique challenges of adopting, deploying, using, and maintaining AI capabilities
  • identify and investigate emerging AI and AI-adjacent technologies that are rapidly transforming the technology landscape

Are you creative, curious, energetic, collaborative, technology-focused, and hard-working? Are you interested in making a difference by bringing innovation to government organizations and beyond? Apply to join our team.

As a Senior Machine Learning Research Scientist, you will specialize in conducting research into the vulnerabilities of AI and ML algorithms and securing against those vulnerabilities.

The Secure AI Lab within the SEI's AI Division focuses on improving the security and robustness of AI systems. As part of the world-class research community at Carnegie Mellon University, the Secure AI Lab conducts and applies cutting-edge research to protect AI systems from adversaries who aim to manipulate the system to learn, do, or reveal something it isn't supposed to.

The Secure AI Lab consists of machine learning research scientists, machine learning engineers, and software developers who work together to solve problems in the following areas:

  • Counter AI Research: Study threat models targeting AI and ML algorithms, understand the behaviors of AI algorithms, identify weak points, and design novel ways to subvert AI and ML systems.
  • AI and ML Algorithm Defense Research: Create practical mitigations and defenses for observed attacks affecting AI and ML algorithms and evaluate the effectiveness of defensive techniques.
  • Applied Adversarial Machine Learning: Advance the state of the art in adversarial machine learning by developing and transitioning capabilities to government sponsors.

Your day-to-day research tasks will include:

  • Identifying and investigating emerging AI and AI-adjacent technologies.
  • Performing and publishing impactful original research in the field of AI and ML algorithm vulnerabilities and securing against those vulnerabilities.
  • Adapting and applying existing research in the field to solve real-world problems.
  • Transitioning and providing guidance on AI capabilities to government sponsors.

Duties:

  • Hands-on research: You'll conduct and lead novel research in applied machine learning and artificial intelligence.
  • Solution development: You'll work with and lead interdisciplinary teams to turn research results into prototype operational capabilities for government customers and stakeholders.
  • Strategy: You'll work with the leadership team and colleagues to plan, develop, and carry out an overall research strategy, and to influence the national research agenda regarding future technology.
  • Collaboration: You'll actively participate on teams of software developers, researchers, designers, and technical leads. You'll build relationships and collaborate with researchers, government customers, and other stakeholders to understand challenges, needs, possible solutions, and research directions.
  • Mentoring: You'll contribute to improving the overall technical capabilities of the Division by mentoring and teaching others, participating in design (software and otherwise) sessions, and sharing insights and wisdom across the SEI AI Division.

Knowledge and Experience:

  • Comprehensive knowledge of machine learning; previous experience in adversarial machine learning preferred but not required
  • A track record of conducting research and applying scientific methods to solve difficult problems
  • Experience leading research projects in novel areas with limited previous work to build upon
  • Ability to work with leadership to plan, develop, and deliver an overall research strategy
  • Strong written and verbal communication skills; ability to convey complex technical ideas in a layperson's terms
  • Proficiency in writing funding proposals or pitching ideas for new research projects
  • Ample experience with publishing written or technical artifacts showcasing your work
  • Strong collaboration skills for working with colleagues and sponsors
  • Willingness to guide and mentor junior team members

Requirements:

  • A bachelor's degree in computer science, statistics, machine learning, electrical engineering, or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years of experience; OR PhD with five (5) years of experience
  • Willingness to work onsite at an SEI facility 5 days per week.
  • Be able to obtain and maintain an active Department of War security clearance.
  • Willing to travel up to 25% of the time to locations outside of your home location. Travel sites include SEI offices in Pittsburgh and Washington, D.C., sponsor sites, and conferences.

Location: Arlington, VA, Pittsburgh, PA

Job Function: Software/Applications Development/Engineering

Position Type: Staff – Regular

Full time/Part time: Full time

Pay Basis: Salary

More Information:
  • Please visit "Why Carnegie Mellon" to learn more about becoming part of an institution inspiring innovations that change the world.
  • Click here to view a listing of employee benefits
  • Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran.
  • Statement of Assurance