Interested in this AI Software Engineer role at Parallax Advanced Research?
Apply Now →Skills & Technologies
About This Role
Senior AI/ML \& Cloud Native Software Engineer
Location: On\-Site \- Dayton
REQUIRED: MUST BE A US CITIZEN and Active TS/SCI Clearance
At Parallax, we believe in advancing science and serving the public good—and we’re looking for team members who do too. If you’re ready to support big ideas through precise execution, we encourage you to apply.
Why This Role Matters
At Parallax Advanced Research, our work is rooted in innovation, collaboration, and national impact. As a nonprofit research institute, we partner with government, academic, and industry clients to solve complex challenges in AI, aerospace, human\-machine teaming, and beyond. Parallax Advanced Research Mission is to deliver innovative research and provide technology, human and business solutions via The Science of Intelligent Teaming™ for government, industry and academic clients. Parallax uses agile software development methods to deliver customized research and development software products in support of Air Force and other service missions.
The Senior AI/ML \& Cloud\-Native Software Engineer will support the NASIC Transformation Collection within the Services of Common Concern (SoCC). This position focuses on the design, development, maintenance, and enhancement of AI/ML services and related cloud\-native capabilities, including AI/ML Service, Advanced Architecture Environment (AAE) Development, Legacy Data Migration, Data Fabrication/Custom Dataflows, and Cross Domain/NASIC Object Transfer Service (NOTS).
The engineer will build and sustain a scalable AI/ML software stack, integrate modern analytic tools, support multiple mission product lines, and deploy solutions into the NASIC Unified Cloud (NUC) and other Department of the Air Force (DAF) cloud environments using the Scrum framework.
In This Role, You Will:
AI/ML Servies \& Models
- Design, implement, and maintain scalable, maintainable, and shareable AI/ML models in alignment with government\-approved datasets and requirements.
- Develop and maintain a scalable AI/ML software stack supporting the full AI/ML lifecycle (data ingestion, training, evaluation, deployment, monitoring).
- Implement reproducible training and evaluation pipelines, documenting procedures and results to support auditability and compliance reviews.
- Define and implement model retraining triggers, update procedures, and versioning practices to ensure ongoing model accuracy and reliability.
- Integrate modernized analytic tools into AI/ML capabilities and expose them through easy\-to\-use interfaces for mission users.
- Implement model monitoring capabilities, including user\-enabled monitoring, model drift detection, and model demand tracking.
- Adhere to industry\-accepted AI/ML development principles, including transparency, accountability, and responsible use of data and AI/ML.
Cloud\-Native Development \& CI/CD
- Design and implement scalable, cloud\-native applications and services using containerization and microservices patterns consistent with NASIC’s approved technology stack.
- Integrate and deploy code and containers into the NASIC Unified Cloud (NUC) via the established CI/CD pipeline across multiple security enclaves (NIPRnet, SIPRnet, JWICS) and other cloud architectures such as DAF CLOUDworks.
- Ensure secure, reliable, and repeatable deployment processes in accordance with DevSecOps best practices and applicable security/compliance standards.
Data Engineering \& Legacy Migration
- Design and implement data ingestion, transformation, and custom dataflow pipelines to support AI/ML workloads and Transformation Collection services.
- Plan and execute legacy data migration into modern architectures and cloud environments, ensuring data quality, integrity, and traceability.
- Leverage diverse, relevant data sources and data types for AI/ML models, including establishing connections to future data sources as needed.
- Provide and manage access to datasets required for training, testing, and validating AI/ML models, ensuring that all data preparation steps are fully documented and reproducible.
System Integration \& Enterprise Services
- Develop and integrate Transformation Collection services (AI/ML Service, AAE Development, Legacy Data Migration, Data Fabrication/Custom Dataflow, NOTS) to promote and enhance data/product exchange and event\-driven architecture.
- Integrate capabilities with Integration Services, ICAM Services, Catalog Services, Collaboration Services, and Enterprise Product Authoring Service (EPAS).
- Collaborate with mission application teams to enable data services across NASIC and the Air Force Intelligence Community (AF IC).
- Assess and document the existing Transformation Collection software baseline and identify modernization, refactoring, and integration opportunities.
- Evaluate and integrate Free\-and\-Open\-Source\-Software (FOSS), Commercial\-Off\-The\-Shelf (COTS), and Government\-Off\-The\-Shelf (GOTS) solutions consistent with the NASIC approved technology stack.
- Ensure all software deliveries and modifications are submitted to the NASIC Depot in accordance with NASIC policies (no unapproved contractor repositories).
Maintenance, Sustainment \& Minor Enhancements
- Perform maintenance, sustainment, and minor enhancements (MS\&E) on delivered operational software within the Transformation Collection.
- Refactor and improve existing codebases to enhance performance, reliability, maintainability, and security.
- Troubleshoot and resolve issues in production and pre\-production environments, working closely with government and contractor teams.
Agile/Scrum \& Documentation
- Work within the Scrum framework as described in the NASIC Software Development Guide (SDG), participating in all Scrum ceremonies (sprint planning, daily stand\-ups, sprint reviews, retrospectives).
- Collaborate with Product Owners and stakeholders to refine, estimate, and implement backlog items.
- Develop high\-quality technical documentation, including architecture descriptions, data pipeline documentation, AI/ML training and evaluation documentation, deployment runbooks, and user enablement materials.
- Support required contract deliverables (CDRLs) and compliance documentation related to Transformation Collection services.
Required Skills and Qualifications
- US Citizenship REQUIRED
- Active TS/SCI Clearance and maintenance REQUIRED
- Bachelor’s degree in Computer Science or related technical field with experience (10\+ years) in software development
- Proficiency in at least one modern programming language commonly used for AI/ML and microservices (e.g., Python, Java).
- Hands\-on experience with at least one major AI/ML framework (e.g., TensorFlow, PyTorch, Scikit\-learn).
- Experience with MLOps practices and tools (e.g., MLFlow, Kubeflow, or similar) for managing the AI/ML lifecycle.
- Strong background in containerization (e.g., Docker) and orchestration (e.g., Kubernetes or equivalent).
- Experience designing and operating CI/CD pipelines (e.g., GitLab CI, Jenkins, GitHub Actions, or similar) for multi\-environment deployments.
- Data engineering experience, including ETL/ELT, custom dataflows, and integration of diverse data sources.
- Experience with secure software development and DevSecOps concepts, including code scanning, vulnerability management, and compliance in multi\-enclave environments.
- Familiarity with integrating FOSS, COTS, and GOTS solutions into an enterprise technology stack.
- 8 years in software engineering, with at least 5 years in DevSecOps and AI/ML
- Proven track record of embedding security in software development
- Demonstrated ability to mentor, lead projects, and influence technical direction
- Strong written and verbal communication skills for cross\-team collaboration
PREFERRED QUALIFICATIONS (KNOWLEDGE, SKILLS, AND ABILITIES)
- MS in Computer Science or related technical field
- Experience working within NASIC, Air Force Intelligence, or broader Intelligence Community environments.
- Familiarity with cross\-domain solutions (CDS) and multi\-level security concepts (NIPR/SIPR/JWICS).
- Experience with event\-driven architectures (e.g., Kafka or similar technologies).
- Prior experience integrating with identity and access management (ICAM), data cataloging, collaboration services, or enterprise content/product authoring systems.
- Knowledge of applicable DoD/IC security controls, STIG requirements, and accreditation processes.
CLEARANCE \& CITIZENSHIP
- Active TS/SCI Clearance and US Citizenship are required
Physical Demands and Work Environment
Physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this position. Reasonable accommodation may be made to enable individuals with disabilities to perform the functions.
- Sitting \& Posture – Ability to sit for long periods with good posture.
- Manual Dexterity – Use of a keyboard, mouse, and office equipment.
- Vision \& Reading – Clear vision (corrected if needed) for screens and documents.
- Hearing \& Communication – Ability to hear and speak clearly for calls and meetings.
- Mobility – Walking short distances, occasional reaching, bending, or lifting light objects.
- Cognitive Ability – Focus, problem\-solving, and multitasking skill
Travel requirements/percentage: 10%
Parallax Advanced Research is an Equal Opportunity Employer, drug free workplace, and complies with ADA regulations as applicable.
Why Join Parallax?
We offer a workplace that values both personal well\-being and professional growth, along with a robust benefits package:
Health Benefits: Medical, dental, and vision coverage
Insurance: 100% employer\-paid life, short\- and long\-term disability
Retirement: Roth or pre\-tax 401(k) with company match \+ annual employer contribution
Education: 100% tuition coverage for employees \+ 80% for dependents
Time Off: 25 days of PTO, flex holidays, paid parental leave, and military leave
Work\-Life Balance: PTO sell\-back program and hybrid flexibility for select positions
At Parallax, we believe in advancing science and serving the public good—and we’re looking for team members who do too. If you’re ready to support big ideas through precise execution, we encourage you to apply.
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Parallax Advanced Research, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Parallax Advanced Research AI Hiring
Parallax Advanced Research has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Beavercreek, OH, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.