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About This Role
Why RTI:
RTI International is an independent, scientific research institute dedicated to improving the human condition. Our vision is to address the world's most critical problems with technical and science-based solutions in pursuit of a better future. Clients rely on us to answer questions that demand an objective and multidisciplinary approach—one that integrates expertise across social, statistical, data, and laboratory sciences, engineering, and other technical disciplines to solve the world’s most challenging problems.
We believe in the promise of science and technical solutions, and we push ourselves every day to deliver on that promise for the good of people, communities, and businesses in the US and around the world. If you are looking for the opportunity to make a real difference, RTI is the place for you. About the Hiring Group: You are:
A Software Engineer with experience building cloud-native applications and applying AI technologies to practical business, research or client needs. You have contributed to systems beyond the proof-of-concept stage and understand the engineering work required to make AI-enabled applications reliable, secure and maintainable.
You are comfortable working through ambiguity, explaining technical trade-offs and collaborating with researchers, data scientists and software engineers. You can distinguish between a compelling demonstration and a solution that is ready to support real users.
You have personally contributed to building and deploying AI-enabled applications, not just experimenting with models or using AI tools as an end user. You can clearly explain the systems you worked on, your individual contribution, the technical decisions you made, and how you evaluated whether the solution worked. What You'll Do: The Work:
You will work closely with internal teams and clients to contribute to the design, development, and deployment of AI‑powered solutions across sectors such as health, energy, and education. You will help translate business and research needs into technical implementations, build and refine multi-step AI workflows, and support the delivery of scalable, secure systems in enterprise environments.
You will contribute to new systems and enhancements that integrate with existing infrastructure, collaborating with colleagues and partners to deliver practical solutions that can be tested, deployed, maintained, and supported over time.
In this role, you will support the development of AI systems and enhancements that integrate with RTI’s research infrastructure—including survey data collection platforms, systematic review pipelines, data quality assurance systems, and proposal development workflows—working closely with data scientists, domain researchers, and software developers to ensure solutions meet the rigor expected in federally regulated and research‑driven environments. Location preference: For this position, we strongly prefer candidates who are located within reasonable driving distance of RTI’s headquarters in Research Triangle Park, North Carolina. Proximity to RTP will allow for periodic in-person collaboration, team meetings, and other on-site activities as needed. As part of the interview process, candidates will be asked to discuss a system they personally helped build. The discussion will cover their individual contribution, key technical decisions, challenges encountered, evaluation approach and results. Candidates will also participate in a job-related technical discussion and exercise. Responsibilities:* AI System & Agent Development. Design, build, and maintain AI‑enabled systems and multi-stepworkflows, including retrieval, tool use, orchestration, and iterative improvement, with a focus on production readiness and maintainability.
- Enterprise & Research Infrastructure Integration. Contribute to AI systems that integrate with and extend RTI’s existing platforms—such as survey data collection, systematic review pipelines, data quality assurance tools, and proposal workflows—working within established architectures and constraints.
- Cloud‑Native Engineering. Develop and deploy cloud‑native services using APIs, containers, and modern CI/CD practices, contributing to scalable, secure, and cost‑aware AI solutions in enterprise environments.
- Workflow Translation & Automation. Translate research, business, and client needs into working AI applications that automate or augment workflows, collaborating closely with data scientists, researchers, and software engineers to ensure practical value and methodological rigor.
- Evaluation, Quality, and Reliability. Contribute to testing and evaluation approaches for AI systems, including performance monitoring, reliability checks, and cost/quality trade‑off analysis appropriate for federally regulated and research‑driven contexts.
- Client & Cross‑Team Collaboration. Participate in technical discussions, workshops, and prototype development with internal teams and clients, helping gather requirements, demonstrate solutions, and iterate based on stakeholder feedback.
- Reusable Patterns & Knowledge Sharing. Document systems, contribute to reusable components and shared abstractions, and support internal knowledge sharing to strengthen RTI’s AI engineering practices over time.
What You'll Need: Minimum Qualifications:* Master's degree and at least three years of relevant work experience building cloud-native applications such as APIs, microservices, containerization, or serverless functions; OR a Bachelor's degree with at least five years of relevant experience building cloud-native applications such as APIs, microservices, containerization, or serverless functions.
- At least two years of programming experience in Python, JavaScript, TypeScript, or similar languages commonly used in AI and backend development.
- Demonstrated experience contributing to the development and deployment of AI- or LLM-based systems, including retrieval pipelines, prompt design, agentic workflows, or workflow automation.
- Experience integrating commercial or open-source AI models into a software application through APIs or model-serving infrastructure.
- Experience developing systems that work with sensitive, restricted, or confidential data, and understanding how data access, privacy, and compliance requirements influence system design.
- Proficiency with shared version control (e.g., Git) and collaborative software development workflows.
- Familiarity with modern deployment and delivery practices, including CI/CD pipelines, containers, and basic infrastructure-as-code concepts.
- Experience working in cross-functional, team-based environments, including participating in technical discussions, reviews, and iterative delivery.
- Strong written and verbal communication skills, with the ability to explain AI system behavior, trade-offs, and limitations to non-technical researchers, clients, and federal stakeholders.
- To qualify, applicants must be legally authorized to work in the United States and should not require, now or in the future, sponsorship for employment visa status (e.g., H-1B visa status, etc.).
Preferred Qualifications:* Experience working with AI orchestration frameworks such as LangChain, LlamaIndex, or similar tools.
- Exposure to AI orchestration or workflow frameworks, or experience building applications that coordinate multiple models, tools, or processing steps. Experience working with one or more cloud platforms such as AWS, Azure, or GCP.
- Experience working with relational and cloud-native database systems (e.g., PostgreSQL, SQL-based systems).
- Experience supporting projects in enterprise, regulated, or research-driven environments.
- Experience contributing to proposals, technical writing, or business development efforts in a research or federal contracting context.
- Proficiency with Unix/Linux command-line environments.
- Interest in growing into more senior AI engineering or technical leadership roles within a collaborative, research‑driven environment.
EEO & Pay Equity Statements:
For San Francisco, CA USA Job Postings Only: Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Further information is available here.
RTI accepts applications to our job openings from candidates with criminal histories or conviction records in accordance with all applicable laws, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
For Applicants in Massachusetts Only: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
The anticipated pay range for this role is listed below. Our pay ranges represent national averages and may vary by location as a geographic differential may be applied to some locations within the United States. RTI considers multiple factors when making an offer including, for example: established salary range, internal budget, business needs, and education and years of work experience possessed by the applicant. Further, salary is merely one element to our offer.
At RTI, we demonstrate our commitment to rewarding individual and team achievement through a total rewards package. This package includes (among other things) a competitive base salary, a generous paid time off policy, merit based annual increases, bonus opportunities and a robust recognition program. Other benefits include a competitive range of insurance plans (including health, dental, life, and short-term and long-term disability), access to a retirement savings program such as a 401(k) plan, paid parental leave for all parents, financial assistance with adoption expenses or infertility treatments, financial reimbursement for education and developmental opportunities, an employee assistance program, and numerous other offerings to support a healthy work-life balance. Hiring Salary Range: $122,000 - $150,000
Salary Context
This $122K-$150K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $193K across 172 roles with salary data).
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 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At RTI International, 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 $220,000 based on 1,448 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($136K) sits 38% below the category median. Disclosed range: $122K to $150K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
RTI International AI Hiring
RTI International has 2 open AI roles right now. They're hiring across Research Scientist, AI Software Engineer. Positions span NC, US, Durham, NC, US. Compensation range: $135K - $150K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
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