Senior Software Engineer, Advanced AI Research & Prototyping

📍 Job Overview

Job Title: Senior Software Engineer, Advanced AI Research & Prototyping

Company: Aptima Inc

Location: Fairborn, Ohio, United States

Job Type: Full-Time

Category: Software Engineering / AI Research & Development

Date Posted: March 12, 2026

Experience Level: 6+ Years Professional Experience (Senior Level)

Remote Status: On-site

🚀 Role Summary

• Architectural design and development of advanced software systems for cutting-edge AI research and experimental prototypes.

• Translating novel AI algorithms, models, and concepts into functional, scalable prototype systems through close collaboration with research scientists.

• Building flexible research platforms to enable rapid experimentation, evaluation, and iteration of AI capabilities.

• Contributing to the development of innovative AI-enabled capabilities across domains such as human-AI teaming, synthetic media analysis, influence modeling, and agent-based systems.

• Providing technical leadership on R&D projects, shaping system architectures, and mentoring junior engineers on best practices.

📝 Enhancement Note: This role is positioned as a Senior Software Engineer with a strong emphasis on advanced AI research and prototyping within the national security sector. The description highlights a blend of hands-on development, system architecture, and technical leadership, requiring a candidate who can bridge the gap between theoretical AI research and practical software implementation. The focus on R&D, prototypes, and government-sponsored research (like DARPA) indicates a need for adaptability, innovation, and a deep understanding of the software development lifecycle in an exploratory environment.

📈 Primary Responsibilities

• Architect and develop advanced software systems to support cutting-edge AI research and experimental prototypes, ensuring scalability and robustness.

• Collaborate closely with AI scientists and researchers to translate novel algorithms, models, and concepts into functional, deployable prototype systems.

• Design and implement flexible research platforms and experimental testbeds that enable rapid iteration, evaluation, and comparative analysis of AI models and capabilities.

• Lead the design and implementation of software architectures for complex research prototypes, including defining APIs, data pipelines, model integration layers, and robust evaluation frameworks.

• Build modular, extensible, and well-structured systems that facilitate rapid integration of new models, analytics, and experimental capabilities by internal researchers and external collaborators.

• Implement and integrate machine learning and AI models within experimental systems and research testbeds, supporting both internal evaluation and integration with external systems for large-scale experiments.

• Develop workflows that support efficient model training, evaluation, experimentation, and comparative analysis, leveraging containerization for reproducible research environments.

• Provide technical leadership on R&D projects, contributing to the strategic direction of system architectures and technical approaches, and fostering collaboration across engineering teams.

• Mentor junior engineers and research staff on software architecture principles, modern engineering practices, and the development of scalable research infrastructure.

• Participate actively in customer engagements, technical reviews, demonstrations, and collaborative research activities, effectively communicating technical concepts to diverse stakeholders.

📝 Enhancement Note: The responsibilities clearly delineate a senior-level role requiring not only strong technical execution but also strategic input into architecture, research translation, and team mentorship. The emphasis on "flexible research platforms," "rapid experimentation," and "translating novel algorithms" points to a need for engineers comfortable with ambiguity and iterative development cycles common in R&D environments. The inclusion of "customer engagements" and "demonstrations" suggests a role that interfaces directly with external stakeholders, demanding strong communication and presentation skills beyond pure technical development.

🎓 Skills & Qualifications

Education:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, AI/ML, or a closely related technical field.

Experience:

• A minimum of 6 years of professional software engineering experience focused on developing complex technical systems.

• Proven experience building complex research prototypes or experimental systems, demonstrating the ability to translate innovative concepts into functional software.

Required Skills:

• Strong proficiency in Python for software development, scripting, and data analysis.

• Deep understanding of modern software engineering practices, including version control (e.g., Git), testing methodologies, and CI/CD principles.

• Experience architecting modular software systems and designing robust APIs or service-based architectures.

• Familiarity with AI/ML technologies and practical experience integrating machine learning models into larger software systems.

• Ability to thrive in ambiguous, exploratory environments and effectively translate evolving research ideas into working software solutions.

• Excellent communication, collaboration, and interpersonal skills, with the ability to work effectively with researchers, engineers, and external partners.

Preferred Skills:

• Experience supporting government-sponsored research programs, such as those managed by DARPA, Department of the Army (DoW) laboratories, or other defense/intelligence agencies.

• Familiarity with advanced AI concepts including large language models (LLMs), agentic AI systems, or multimodal machine learning techniques.

• Experience building research testbeds, comprehensive evaluation frameworks, or sophisticated simulation environments.

• Background in distributed systems, high-performance computing (HPC), or developing scalable experimentation infrastructure.

• Experience contributing to technical publications, research demonstrations, or conference submissions in relevant AI/ML or software engineering fields.

• Exposure to cloud computing platforms (e.g., AWS, Azure, GCP) or hybrid research infrastructure environments.

• A strong interest in working at the intersection of advanced AI research, national security applications, and emerging technologies.

📝 Enhancement Note: The "Nice to Have" section provides clear signals for candidates seeking to differentiate themselves, particularly those with experience in government R&D. The specific mention of DARPA and DoW, alongside topics like LLMs and agentic systems, indicates the company's focus areas. The requirement for a security clearance is critical and should be a primary consideration for applicants. The emphasis on translating "evolving research ideas into working software" highlights the need for adaptability and a problem-solving mindset.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrations of previously developed research prototypes or experimental systems, showcasing the ability to translate complex concepts into functional software.

• Examples of modular software architectures and API designs, illustrating a systematic approach to system design and extensibility.

• Case studies detailing the integration of machine learning or AI models into software systems, highlighting the process and outcomes.

• Documentation or descriptions of contributions to scalable research platforms or testbeds, emphasizing their role in facilitating experimentation and iteration.

Process Documentation:

• Showcase a structured approach to translating research requirements into actionable software development plans.

• Illustrate workflows developed for model training, evaluation, and comparative analysis, demonstrating process efficiency and rigor.

• Provide examples of how system architectures were designed to balance rapid research iteration with maintainable and well-structured engineering practices.

• Document contributions to evaluation frameworks or data pipelines that support robust system assessment and performance measurement.

📝 Enhancement Note: For a role focused on advanced AI research and prototyping, a portfolio is crucial for demonstrating practical application of theoretical knowledge. Candidates should prepare to showcase projects that highlight their ability to navigate ambiguity, build foundational systems for research, and integrate complex AI components. Emphasis should be placed on the adaptability and iterative nature of the development process, rather than solely on polished, production-ready software.

💵 Compensation & Benefits

Salary Range:

Benefits:

• Comprehensive health, dental, and vision insurance plans.

• Retirement savings plan with company matching contributions (e.g., 401k).

• Paid time off (PTO), including vacation, sick leave, and holidays.

• Opportunities for professional development, training, and conference attendance.

• Potential for performance-based bonuses and stock options, depending on company policy and individual contribution.

• Support for obtaining and maintaining U.S. Government security clearances.

Working Hours:

• Standard full-time work schedule is typically 40 hours per week.

• Flexibility may be offered to accommodate research needs and project deadlines, with potential for occasional overtime during critical project phases or demonstrations.

• The on-site nature of the role emphasizes collaboration and direct engagement within the research environment.

📝 Enhancement Note: The salary range is estimated based on the "Senior Software Engineer" title, 6+ years of experience, the specified location (Fairborn, OH), and the industry (AI Research, Government Contracting). This estimate uses data from various compensation aggregators and industry surveys for similar roles in the region. Benefits are standard for a professional engineering position in the US, with specific emphasis on those relevant to government contracting (security clearance support) and R&D roles (professional development).

🎯 Team & Company Context

🏢 Company Culture

Industry: National Security / Defense Technology / Artificial Intelligence Research & Development. Aptima Inc. operates at the forefront of technological innovation, focusing on solutions that enhance human potential and performance within mission-critical environments for national security applications.

Company Size: Aptima Inc. is a medium-sized company, typically employing between 201-500 employees. This size often fosters a culture that balances the agility and close-knit feel of a smaller organization with the resources and project scope of a larger entity.

Founded: Aptima Inc. was founded in 1997. With over two decades of operation, the company has established a strong track record and deep expertise in its specialized fields, particularly in areas involving human-computer interaction, cognitive science, and AI.

Team Structure:

• The AI Research & Prototyping team is likely a multidisciplinary group comprising AI researchers, cognitive scientists, and software engineers.

• The Senior Software Engineer will report to an Engineering Lead or R&D Director, working within project-specific teams.

Methodology:

• Aptima emphasizes a research-driven approach, focusing on engineering scalable solutions that fuse technological innovation with human potential.

• Data analysis and insights are critical for evaluating AI models and prototype performance.

• Workflow planning and optimization are key to managing the iterative process of R&D and prototype development.

• Automation and efficiency practices are leveraged to accelerate experimentation and streamline development cycles.

Company Website: https://www.aptima.com/

📝 Enhancement Note: Aptima's focus on the "human component" in national security is a key differentiator. The company's age (founded 1997) suggests stability and deep-rooted expertise. The medium size implies a culture that might offer more direct impact and visibility for individual contributors compared to very large corporations, while still having the capacity for significant R&D projects. The company's core values (Integrity, Ingenuity, Excellence, Respect, Engagement, Teamwork) should inform how candidates approach their interactions and demonstrate their fit.

📈 Career & Growth Analysis

Operations Career Level: This role is classified as a Senior Software Engineer, indicating a mid-to-late career stage. It involves significant technical expertise, the ability to lead technical aspects of projects, and potentially mentor junior staff. The role requires a deep understanding of software architecture, AI/ML integration, and the practicalities of building research prototypes.

Reporting Structure: The Senior Software Engineer will likely report to an Engineering Manager, Director of Engineering, or a Program Manager overseeing R&D initiatives. They will work within project teams, collaborating closely with AI researchers, cognitive scientists, and other engineers. Direct reporting lines are typically within the engineering department, with project-specific work structures influencing daily interactions.

Operations Impact: The impact of this role is significant, directly contributing to the advancement of AI capabilities for national security. By translating cutting-edge research into functional prototypes, this engineer enables critical technology demonstrations, informs future R&D investments, and ultimately supports the development of solutions that enhance national security operations. Their work can influence strategic decisions regarding AI technology adoption and development pathways.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific AI domains like human-AI teaming, agentic systems, or multimodal analytics through hands-on project work and targeted learning.

• Leadership Development: Progress into roles such as Technical Lead, Principal Engineer, or Engineering Manager, taking on greater responsibility for project architecture, team guidance, and strategic technical planning.

• Cross-Functional Expertise: Develop a stronger understanding of cognitive science and AI research methodologies, becoming a valuable bridge between scientific discovery and engineering implementation.

• Industry Engagement: Contribute to publications, presentations, and demonstrations, building professional visibility within the AI and national security research communities.

• Advanced Clearance: Pursue higher levels of security clearance, opening doors to more sensitive and impactful projects within the national security domain.

📝 Enhancement Note: The growth path for a Senior Software Engineer at an R&D-focused company like Aptima often involves deepening technical expertise or moving into leadership. The "Nice to Have" section in the requirements provides a roadmap for developing specialized skills. The company's focus on government clients suggests that career progression might also be tied to the ability to handle increasingly complex and sensitive projects, often requiring higher security clearances.

🌐 Work Environment

Office Type: This role is on-site, suggesting a traditional office environment designed to foster collaboration and focused work. Aptima's focus on R&D and national security implies a professional, secure, and potentially highly collaborative workspace.

Office Location(s): The primary office is located at 2555 University Blvd, Fairborn, OH 45324. This location is in proximity to Wright-Patterson Air Force Base, a major hub for defense research and development, which is highly relevant given Aptima's industry.

Workspace Context:

• Collaborative Environment: The on-site nature facilitates direct interaction with AI researchers, cognitive scientists, and fellow engineers, promoting dynamic problem-solving and knowledge sharing.

• Operations Tools & Technology: Access to high-performance computing resources, specialized software development tools, and secure network infrastructure necessary for AI research and prototype development.

• Operations Team Interaction: Regular team meetings, brainstorming sessions, and project reviews are expected, fostering a culture of continuous feedback and collective innovation.

Work Schedule: The standard work schedule is 40 hours per week, with the understanding that flexibility may be required for critical project phases, demonstrations, or to meet sponsor deadlines. This balance allows for dedicated work time while acknowledging the dynamic nature of R&D projects.

📝 Enhancement Note: The Fairborn, Ohio location is strategically significant, being near a major Air Force base. This suggests potential synergies with government research initiatives and a community of professionals in the defense technology sector. The on-site requirement points to the company's emphasis on in-person collaboration, essential for the complex, interdisciplinary work involved in AI research and prototyping.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or a recruiter will review applications, assessing basic qualifications, experience, and alignment with the role's core requirements, including security clearance eligibility.

• Technical Interview(s): Candidates will likely undergo one or more technical interviews. These may include:

  • Discussions on software architecture principles, system design, and API development.
  • Coding challenges or live coding sessions focusing on Python proficiency and problem-solving.
  • Deep dives into AI/ML concepts, model integration, and experience with prototyping.
  • Behavioral questions assessing collaboration, adaptability, and problem-solving in ambiguous environments.

• Portfolio Review: Candidates will be asked to present and discuss their relevant projects, showcasing their contributions to research prototypes, system architectures, and AI integrations. This is a critical stage to demonstrate practical application of skills.

• Team/Hiring Manager Interview: A final interview with the hiring manager or key team members to assess cultural fit, leadership potential, and alignment with team dynamics.

• Offer and Clearance Process: Successful candidates will receive an offer, contingent upon obtaining and maintaining the required U.S. Government security clearance.

Portfolio Review Tips:

• Showcase Relevance: Prioritize projects that directly relate to AI research, prototyping, system architecture, and Python development.

• Demonstrate Process: Clearly articulate your role, the challenges faced, the technical approach you took, and the outcomes achieved. Emphasize iterative development and problem-solving in ambiguous research settings.

• Highlight Architecture: For system architecture examples, explain design choices, API strategies, and how modularity was achieved.

• Detail AI Integration: For AI/ML projects, explain the models used, how they were integrated, and the performance metrics or evaluations conducted.

• Quantify Impact: Whenever possible, use metrics to demonstrate the impact of your work (e.g., performance improvements, reduced complexity, successful demonstrations).

Challenge Preparation:

• Coding Proficiency: Be prepared for Python coding exercises that test algorithmic thinking and practical implementation skills. Review data structures and common algorithms.

• System Design: Practice designing scalable, modular systems and APIs. Consider trade-offs, error handling, and data flow.

• AI/ML Concepts: Brush up on fundamental AI/ML concepts, common model types, and the challenges of integrating them into software. Be ready to discuss LLMs, agentic systems, or multimodal ML if you have experience.

• Behavioral Scenarios: Prepare examples for questions about handling ambiguity, collaborating with researchers, mentoring junior engineers, and overcoming technical challenges.

📝 Enhancement Note: The interview process will heavily scrutinize practical experience in building research prototypes and integrating AI. Candidates should be ready to articulate their thought processes, design decisions, and how they navigated the unique challenges of R&D environments. The portfolio review is a key differentiator, requiring candidates to not just list projects but to explain their contributions and impact effectively.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (primary), potentially others like C++, Java, or Go depending on specific project needs.

• Version Control: Git (e.g., GitHub, GitLab, Bitbucket) is standard for collaborative development.

• Containerization: Docker is explicitly mentioned, indicating its importance for creating reproducible research environments. Kubernetes may be a plus for orchestration.

• Development Environments: IDEs like VS Code, PyCharm, or others suitable for Python development.

Analytics & Reporting:

• Data Analysis Libraries: NumPy, Pandas, SciPy for data manipulation and scientific computing.

• Machine Learning Frameworks: TensorFlow, PyTorch, scikit-learn for model development and integration.

• Visualization Tools: Matplotlib, Seaborn, Plotly for data visualization and reporting on experimental results.

CRM & Automation:

• While not explicitly CRM-focused, concepts of workflow automation and system integration are critical.

• API Development Frameworks: Flask, Django (Python-based) for building APIs and microservices.

• Messaging Queues: Potentially tools like RabbitMQ or Kafka for asynchronous communication in distributed systems.

• CI/CD Tools: Jenkins, GitLab CI, GitHub Actions for automating build, test, and deployment pipelines for prototypes.

📝 Enhancement Note: The technology stack is heavily weighted towards Python and the AI/ML ecosystem. Emphasis on Docker points to a need for modern deployment practices even for prototypes. The mention of "research platforms" and "experimental systems" suggests that familiarity with tools that facilitate rapid iteration and data analysis is paramount. Experience with cloud platforms (AWS, Azure, GCP) is a "nice to have," indicating a potential for hybrid or cloud-based deployments in the future.

👥 Team Culture & Values

Operations Values:

• Integrity: Upholding ethical standards in research, development, and collaboration, especially within the national security context.

• Ingenuity: Fostering creativity and innovative problem-solving to address complex challenges in AI research and system design.

• Excellence: Striving for high-quality engineering and impactful research outcomes, delivering robust and reliable prototypes.

• Respect: Valuing diverse perspectives and fostering a supportive environment for all team members, including researchers and engineers.

• Engagement: Actively participating in projects, contributing to discussions, and staying committed to achieving team and organizational goals.

• Teamwork: Collaborating effectively across disciplines (AI research, cognitive science, engineering) to achieve shared objectives and drive innovation.

Collaboration Style:

• Interdisciplinary: Strong emphasis on cross-functional collaboration, requiring engineers to effectively communicate and work with scientists and researchers.

• Iterative & Experimental: A culture that embraces experimentation, feedback, and continuous improvement, particularly in the R&D and prototyping phases.

• Knowledge Sharing: Encouraging the sharing of insights, best practices, and lessons learned within project teams and across the broader engineering organization.

• Customer-Focused: A commitment to understanding and meeting the needs of sponsors and operational partners, often involving direct engagement and demonstrations.

📝 Enhancement Note: Aptima's stated core values provide a strong framework for understanding the desired team culture. Candidates should be prepared to demonstrate how they embody these values, particularly Ingenuity, Excellence, and Teamwork, in their professional experience. The collaborative nature of the work, bridging science and engineering, means that strong interpersonal and communication skills are as critical as technical prowess.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating Ambiguity: Translating nascent, evolving AI research concepts into functional software requires comfort with uncertainty and the ability to iterate based on new findings.

• Bridging Research and Engineering: Effectively translating complex scientific algorithms and models into robust, scalable software systems can be challenging.

• Security Clearance: Obtaining and maintaining a U.S. Government security clearance is a prerequisite and ongoing requirement, which can be a process for some candidates.

• Rapid Technology Evolution: Keeping pace with the fast-changing landscape of AI and machine learning research requires continuous learning and adaptation.

• Balancing Iteration and Stability: Developing prototypes rapidly while ensuring maintainable code and stable research environments presents a continuous engineering challenge.

Learning & Development Opportunities:

• Advanced AI/ML Training: Opportunities to learn about cutting-edge AI techniques, including LLMs, agentic systems, and multimodal ML, through project work and company-sponsored training.

• Secure Development Practices: Gaining experience with secure software development lifecycles and best practices within a government contracting environment.

• Cross-Disciplinary Learning: Opportunities to learn from cognitive scientists and AI researchers, deepening understanding of human-AI interaction and cognitive modeling.

• Industry Conferences & Certifications: Support for attending relevant AI, software engineering, and national security technology conferences, as well as pursuing professional certifications.

• Mentorship: Access to experienced senior engineers and researchers who can provide guidance on technical challenges, career development, and navigating complex R&D projects.

📝 Enhancement Note: The challenges highlight the dynamic and often uncertain nature of advanced R&D. Candidates should be prepared to discuss how they have successfully navigated similar situations. The growth opportunities are directly tied to Aptima's mission and the evolving field of AI, offering a clear path for continuous professional development.

💡 Interview Preparation

Strategy Questions:

• AI Research Translation: "Describe a time you had to translate a complex research concept or algorithm into a functional software prototype. What were the key challenges, and how did you overcome them?" (Focus on your process, adaptability, and problem-solving.)

• System Architecture for R&D: "How would you design a flexible software architecture for an AI research platform that needs to accommodate rapidly evolving models and experimental requirements?" (Discuss modularity, APIs, data pipelines, and extensibility.)

• Collaboration with Scientists: "How do you approach working with researchers or scientists who may have different technical backgrounds or priorities than engineers? Provide an example." (Highlight communication, translation of technical terms, and shared goal alignment.)

Company & Culture Questions:

• Aptima's Mission: "What interests you most about Aptima's mission in national security and its focus on human-AI teaming?" (Show genuine interest and understanding of the company's domain.)

• Handling Ambiguity: "Describe a situation where you had to work on a project with unclear requirements or evolving goals. How did you manage this ambiguity and ensure progress?" (Relate to the R&D nature of the role.)

• Core Values: "How do you embody values like Ingenuity, Excellence, or Teamwork in your daily work?" (Connect your past experiences to Aptima's stated values.)

Portfolio Presentation Strategy:

• Storytelling: Structure your portfolio presentations as narratives. For each project, explain the problem, your specific role and contributions, the technical approach, the challenges, and the quantifiable outcomes or lessons learned.

• Visual Aids: Use diagrams, code snippets (if appropriate and not proprietary), or demos to illustrate your points. For system architecture, flowcharts are very effective.

• Focus on Impact: Clearly articulate the impact of your work, whether it's enabling further research, demonstrating a capability, or improving efficiency. Use metrics where possible.

• Technical Depth: Be prepared to answer detailed technical questions about your projects, demonstrating a deep understanding of the technologies and design choices.

• Adaptability: Emphasize how your projects demonstrate adaptability, learning new technologies, and working effectively in dynamic R&D environments.

📝 Enhancement Note: Interview preparation should focus on demonstrating a strong blend of technical depth in software engineering and AI, coupled with the soft skills needed for R&D and collaboration. The portfolio presentation is where candidates can truly shine by illustrating their practical problem-solving abilities and their experience with the specific types of challenges Aptima addresses.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided application link on the Aptima Inc. careers portal.

• Tailor Your Resume: Customize your resume to highlight experience in Python, AI/ML integration, software architecture, research prototyping, containerization (Docker), and any government/defense R&D projects. Use keywords from the job description.

• Prepare Your Portfolio: Select 2-3 key projects that best showcase your skills in building experimental systems, designing APIs, integrating ML models, and working in research contexts. Be ready to discuss them in detail.

• Research Aptima: Familiarize yourself with Aptima's mission, core values, recent projects, and their role in the national security sector. Understand their emphasis on human-AI teaming.

• Practice Interview Questions: Prepare answers for common technical, behavioral, and situational questions, particularly those related to translating research into software, handling ambiguity, and collaborating across disciplines.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.