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Research Scientist Computer Science Jobs (NOW HIRING)

Kasmo Global

Applied AI Research Scientist

Irving, TX · On-site

... Science / Computer Engineering (Remote) Exp - 5+ Yrs Job Overview We are seeking skilled and motivated Applied AI Research Scientists in Computer Science and Computer Engineering with an MS or Ph.D ...

Anduril Industries

Research Scientist

Huntsville, AL · On-site

D. in Applied or Computational Mathematics, Electrical Engineering, Computer Science, Controls and Dynamical Systems, Aerospace Engineering, Statistics and Probability, or a related field. • A ...

Anduril Industries

Research Scientist

Huntsville, AL · On-site

D. in Applied or Computational Mathematics, Electrical Engineering, Computer Science, Controls and Dynamical Systems, Aerospace Engineering, Statistics and Probability, or a related field. • A ...

Anduril Industries

Research Scientist

Broomfield, CO · On-site

D. in Applied or Computational Mathematics, Electrical Engineering, Computer Science, Controls and Dynamical Systems, Aerospace Engineering, Statistics and Probability, or a related field. • A ...

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How much do research scientist computer science jobs pay per year?

As of May 30, 2026, the average yearly pay for research scientist computer science in the United States is $130,117.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,500.00 and $173,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Research Scientist in Computer Science, and why are they important?

To thrive as a Research Scientist in Computer Science, you need advanced knowledge of algorithms, data structures, and computational theory, typically supported by a Ph.D. or relevant graduate degree. Proficiency with programming languages (such as Python, C++, or Java), data analysis tools, and version control systems is essential, and experience with machine learning frameworks or high-performance computing can be advantageous. Critical thinking, creativity, and effective communication are vital soft skills for formulating innovative research questions and collaborating across multidisciplinary teams. These skills enable the development of pioneering solutions, clear dissemination of findings, and significant contributions to the advancement of technology.

How do Research Scientists in Computer Science typically collaborate with other professionals on interdisciplinary projects?

Research Scientists in Computer Science often work closely with experts from diverse fields such as data science, engineering, healthcare, or social sciences, depending on the project's focus. Collaboration usually involves joint problem-solving, sharing domain-specific knowledge, and integrating different methodologies to address complex research questions. Communication skills are crucial, as scientists must translate technical concepts for non-technical stakeholders and ensure all team members are aligned on goals and deliverables. Regular meetings, shared documentation, and collaborative software tools are commonly used to facilitate effective teamwork.

What does a Research Scientist in Computer Science do?

A Research Scientist in Computer Science explores new theories, models, and technologies to advance the field. They conduct experiments, analyze data, and publish findings in scientific journals or conferences. Their work often involves collaborating with other researchers, developing prototypes, and contributing to innovations in areas like artificial intelligence, machine learning, cybersecurity, or data science. The goal is to solve complex problems and push the boundaries of what computers and algorithms can achieve.

What is the difference between Research Scientist Computer Science vs Data Scientist?

AspectResearch Scientist Computer ScienceData Scientist
Required CredentialsAdvanced degrees in CS or related fields, research experienceDegree in CS, statistics, or related fields; data analysis certifications
Work EnvironmentResearch labs, academia, R&D departmentsCorporate offices, tech companies, analytics firms
Employer & Industry UsageUniversities, research institutions, tech companiesBusiness, finance, healthcare, tech industries
Common Search & Comparison IntentUnderstanding research roles in CSData analysis and application roles

Research Scientist Computer Science focuses on advancing knowledge through research, often in academic or R&D settings, requiring strong research credentials. Data Scientist emphasizes analyzing and interpreting data to inform business decisions, typically in industry settings. While both roles require technical skills, their work environments and objectives differ significantly.

More about Research Scientist Computer Science jobs
What cities are hiring for Research Scientist Computer Science jobs? Cities with the most Research Scientist Computer Science job openings:
What states have the most Research Scientist Computer Science jobs? States with the most job openings for Research Scientist Computer Science jobs include:
What job categories do people searching Research Scientist Computer Science jobs look for? The top searched job categories for Research Scientist Computer Science jobs are:
Research Scientist Computer Science jobs near you
Infographic showing various Research Scientist Computer Science job openings in the United States as of May 2026, with employment types broken down into 97% Full Time, and 3% Contract. Highlights an 29% Physical, 14% Hybrid, and 57% Remote job distribution, with an average salary of $130,117 per year, or $62.6 per hour.

Applied AI Research Scientist

Kasmo Global

Irving, TX • On-site

Full-time

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

Job description

Job Title: Applied AI Research Scientist - Computer Science / Computer Engineering (Remote)
Exp - 5+ Yrs
Job Overview
We are seeking skilled and motivated Applied AI Research Scientists in Computer Science and Computer Engineering with an MS or Ph.D. in a relevant technical field. In this role, you will contribute to the design, validation, and execution of expert-level evaluation tasks that probe the limits of state-of-the-art AI systems. Your work will focus on creating headroom-level, rigorously verifiable evaluation questions across hardware, systems, and computing domains to assess and stress-test advanced multimodal and reasoning-capable AI models. This position requires deep domain expertise, strong analytical rigor, and the ability to translate complex technical concepts into precise, evaluable challenges that expose model limitations beyond surface-level reasoning. You will work closely with a collaborative, cross-functional team and are expected to be a reliable team player who is highly detail-oriented and committed to accuracy and quality.
Key Responsibilities
Design headroom-level evaluation questions requiring advanced reasoning and graduate-level domain expertise in CS/CE
• Ensure all tasks are objectively verifiable with clear, definitive ground-truth answers
Develop high-quality multimodal prompts, including accurate technical diagrams or visuals when appropriate
Identify and document model headroom by conducting structured side-by-side evaluations of SOTA models such as Gemini and ChatGPT
Document model failures and reasoning gaps, provide correct solutions, and maintain accurate records of prompts, answers, and evaluation results in shared tracking systems
Qualifications
MS or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, Information Technology, Data Science, or a closely related field
Strong expertise in two or more CS/CE domains such as hardware, computer architecture, systems, VLSI design, embedded systems and IoT, operating systems, compilers, systems security, or AI/ML
Proven experience with applied AI research, technical evaluation, or research-driven problem formulation in real-world or production-oriented settings
Strong programming proficiency, with experience in Python for analysis, verification, and evaluation workflows
Strong written communication skills and the ability to collaborate effectively as a detail-oriented team player
Familiarity with modern AI model capabilities, limitations, and benchmarking practices is a strong plus,
Please let me know if you have any questions.

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