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Phd Software Engineer Jobs in Washington (NOW HIRING)

Xometry

Staff Software Engineer

North Bethesda, MD · On-site

S. or PhD) in computer science, machine learning, AI, or a related field is preferred and may substitute for some years of experience * Demonstrable understanding of software engineering principles ...

STR

Senior Software Engineer

Arlington, VA

$140K - $170K/yr

Work closely with PhD scientists and engineers to translate complex mathematical models and research into production-grade, scalable software. * Own Your Growth: Beyond your initial project, your ...

Cmu

AI Security Software Engineer

Arlington, VA · On-site +1

OR PhD in the same fields with two (2) years of experience * Understanding of software engineering principles and system design * Experience with containerization and microservices architectures

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Phd Software Engineer information

See Washington salary details

$71.9K

$167.1K

$232.7K

How much do phd software engineer jobs pay per year?

As of Aug 8, 2026, the average yearly pay for phd software engineer in Washington is $167,085.00, according to ZipRecruiter salary data. Most workers in this role earn between $135,900.00 and $195,900.00 per year, depending on experience, location, and employer.

What is a PhD software engineer?

A PhD Software Engineer is a professional who has completed a Doctor of Philosophy (PhD) degree specializing in computer science, software engineering, or a related field, and works in designing, developing, and optimizing software systems. They often engage in advanced research, develop innovative algorithms, and solve complex technical problems. Their expertise is typically utilized in roles that require deep technical knowledge, research skills, and the ability to push the boundaries of current technology. PhD Software Engineers are commonly found in academia, research institutions, and leading technology companies.

What are the key skills and qualifications needed to thrive as a PhD software engineer?

A PhD Software Engineer requires advanced programming expertise, strong analytical and research skills, and typically a doctorate in computer science or a related field. Familiarity with specialized programming languages, version control systems like Git, and experience with research-oriented software tools are common technical requirements. Exceptional problem-solving, collaboration, and communication skills help bridge the gap between research and practical application. These abilities are crucial for driving innovation, translating complex theories into scalable solutions, and contributing to cutting-edge technology projects.

What does a PhD software engineer do?

As a PhD Software Engineer, you are often entrusted with tackling complex problems and leading research-driven projects that require advanced analytical and technical skills. Your daily work may involve designing novel algorithms, conducting experiments, and collaborating closely with cross-functional teams such as data scientists and product managers. Additionally, you might mentor junior engineers and help shape the technical direction of your team. This role leverages your research background to bridge the gap between academic innovation and practical software solutions.
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Phd Software Engineer jobs near you
Infographic showing various Phd Software Engineer job openings in Washington as of August 2026, with employment types broken down into 17% Internship, 66% Full Time, and 17% Contract. Highlights an 82% In-person, and 18% Hybrid job distribution, with an average salary of $167,085 per year, or $80.3 per hour.

AI Security Software Engineer

Carnegie Mellon University

Arlington, VA • On-site

Full-time

Re-posted 21 days ago

Carnegie Mellon University rating

8.6

Company rating: 8.6 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

67th of 616 rated colleges and universities

Job description

Job Summary:
Carnegie Mellon University is seeking an AI Security Software Engineer within the CERT Division of the Software Engineering Institute. The role involves developing machine learning-based prototypes and tools for AI security applications, collaborating with researchers to design experimental solutions, and applying software engineering best practices to build scalable systems.
Responsibilities:
• Develop machine learning–based prototypes, tools, and systems for AI security applications, demonstrating strong expertise in ML development and deployment
• Collaborate with researchers and stakeholders to design and execute experimental AI security solutions, communicating effectively across technical and non-technical audiences
• Apply software engineering best practices to build scalable, maintainable systems, grounded design principles
• Process and analyze large, diverse cybersecurity datasets (e.g., malware, NetFlow, incident data), using strong analytical and problem-solving skills
• Support AI red teaming and adversarial machine learning initiatives, applying an innovative and research-driven mindset
• Translate research concepts into practical, operational capabilities, with the ability to work independently and as part of a collaborative team
Qualifications:
Required:
• BS in computer science, machine learning, cybersecurity, statistics, or related discipline with eight (8) years of experience; OR MS in the same fields with five (5) years of experience; OR PhD in the same fields with two (2) years of experience
• Understanding of software engineering principles and system design
• Experience with containerization and microservices architectures
• Travel to various locations to support the SEI’s overall mission. This includes within the SEI and CMU community, sponsor sites, conferences, and offsite meetings on occasion (5%)
• You will be subject to a background check and will need to obtain and maintain a Department of War (DoW) security clearance
• Develop machine learning–based prototypes, tools, and systems for AI security applications, demonstrating strong expertise in ML development and deployment
• Collaborate with researchers and stakeholders to design and execute experimental AI security solutions, communicating effectively across technical and non-technical audiences
• Apply software engineering best practices to build scalable, maintainable systems, grounded design principles
• Process and analyze large, diverse cybersecurity datasets (e.g., malware, NetFlow, incident data), using strong analytical and problem-solving skills
• Support AI red teaming and adversarial machine learning initiatives, applying an innovative and research-driven mindset
• Translate research concepts into practical, operational capabilities, with the ability to work independently and as part of a collaborative team
Preferred:
• Experience applying statistical modeling and advanced data analytics techniques
• Background in developing AI/ML solutions in real-world settings
• Familiarity with applied machine learning domains (e.g., natural language processing, computer vision, autonomy, audio analysis)
• Experience and knowledge in cybersecurity best practices
• Demonstrated ability to quickly learn and adapt to new technologies and domains
Company:
Carnegie Mellon University is a research university offering programs and research across engineering, science, arts, and business. Founded in 1900, the company is headquartered in Pittsburgh, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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