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About This Role
Software Engineer 3 - (Python, AI, Vulnerability Research)
Clearance: TS/SCI - Polygraph required
Position ID: 20-16-056-SWE3
Location: Columbia, Maryland
Description:
We are seeking an experienced Software Engineer to design, develop, and integrate AI-enabled capabilities supporting computer network exploitation (CNE) and vulnerability research (VR). This role combines strong software engineering fundamentals with practical knowledge of vulnerability research, program analysis, and AI-assisted research workflows. We are particularly interested in candidates with hands-on experience conducting dynamic vulnerability research in isolated sandbox environments. You will independently own complex features and subsystems, translate mission needs into technical designs, and deliver reliable software from prototype through deployment. You will develop agentic AI harnesses that connect large language models, research tools, instrumented execution environments, and backend services to support repeatable, evidence-driven analysis. Working with vulnerability researchers, AI/ML engineers, and mission stakeholders, you will make sound architectural decisions, evaluate AI-generated findings, and improve the quality and effectiveness of research workflows. This position also includes mentoring engineers and contributing to technical direction and engineering standards.
- Design, develop, and maintain AI-enabled software and research tooling supporting CNE and vulnerability research workflows.
- Own the design and delivery of complex features and subsystems, including requirements refinement, implementation, testing, deployment, and sustainment.
- Develop and integrate agentic AI harnesses that orchestrate research tools, manage execution state, collect evidence, and evaluate results.
- Build and extend sandbox-based dynamic analysis capabilities for executing and observing software, capturing runtime behavior, and reproducing suspected vulnerabilities.
- Integrate debugging, instrumentation, fuzzing, crash analysis, and related research tools into automated or researcher-directed workflows.
- Establish repeatable evaluations that measure finding validity, reproducibility, false-positive rates, and the usefulness of AI-assisted analysis.
- Develop backend services, APIs, data pipelines, and user interfaces that make research capabilities accessible and results traceable.
- Troubleshoot complex issues across application code, AI integrations, research tooling, and execution environments; improve performance, reliability, and observability.
- Lead design and code reviews within assigned areas, document technical decisions, mentor engineers, and communicate progress, risks, and tradeoffs to stakeholders.
Position Required Skills:
- Demonstrated experience independently designing, implementing, testing, and delivering complex software features or subsystems, with sound judgment about architecture, maintainability, and operational requirements.
- Proficiency in Python and experience building maintainable, tested software using modular design and appropriate object-oriented programming practices.
- Demonstrated expertise in AI-enabled CNE or vulnerability research, including integrating AI capabilities into research workflows and critically evaluating their outputs.
- Experience with vulnerability research or software analysis techniques, such as reverse engineering, debugging, dynamic analysis, fuzzing, or crash triage.
- Working knowledge of isolated execution environments and dynamic analysis concepts, including runtime instrumentation, execution tracing, environment reproducibility, and evidence collection.
- Experience developing LLM applications or agentic systems that use external tools, structured outputs, workflow state, error handling, and evaluation.
- Experience developing backend services and REST APIs using frameworks such as FastAPI, Flask, or Django, and working with relational databases such as PostgreSQL.
- Experience working in Linux environments, with an understanding of operating system behavior, processes, memory, and networking relevant to software analysis.
- Experience using Git, automated testing, CI/CD, and code review to deliver reliable software collaboratively.
- Ability to translate ambiguous requirements into actionable technical work, explain technical tradeoffs, and provide effective guidance to other engineers.
Nice to Haves
- Strongly preferred: Hands-on experience conducting dynamic vulnerability research in sandbox environments, including configuring instrumented targets, controlling execution, capturing runtime evidence, and reproducing findings across clean environment resets.
- Experience building AI agents that interact with debuggers, fuzzers, emulators, or other analysis tools within controlled research environments.
- Experience with virtualization, emulation, snapshots, and automated provisioning of repeatable analysis environments.
- Experience with C/C++, assembly, binary analysis, memory corruption analysis, or operating system internals.
- Experience designing evaluation datasets and benchmarks for AI-assisted vulnerability discovery, triage, or validation.
- Experience with retrieval-augmented generation, vector databases, or search platforms such as Elasticsearch, Qdrant, or ChromaDB.
- Experience building researcher-facing interfaces using TypeScript, React, or comparable technologies.
- Contributions to vulnerability research, security tooling, open-source projects, or relevant technical publications.
SWE3 Qualifications:
A minimum of twelve (12) years of experience as a SWE in programs and contracts of similar scope, type, and complexity is required. Bachelor's degree in Computer Science or related discipline from an accredited college or university is required. Four (4) years of additional SWE experience on projects with similar software processes may be substituted for a bachelor's degree.
Salary Range: $241k-$295k (Annually)
The range displayed above is a likely salary range for this position. This is not, however, a guarantee of compensation or salary. Rather, salary will be set based on experience, geographic location and possible contractual requirements and could fall outside of this range.
Akina is a Woman Owned, Service Disabled, Veteran Owned, Small Business, looking for talented and ambitious individuals to join our team. We offer a generous compensation package that includes 24 days PTO accrued annually and 11 federal holidays. Our 401k is 100% vested on your start date and the company makes a direct contribution worth 10% of your salary. Akina covers 100% of healthcare costs for employees and 50% toward dependents. We offer educational assistance towards college classes and will cover costs associated with job related training and certifications.
Akina is committed to excellence and creating innovative and flexible solutions for our clients. We are a small company with an open ear to our employees' needs in order to attract and retain quality talent that enables our customer's mission.
We are an equal employment opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.
www.akina-inc.com/careers
Salary Context
This $241K-$295K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $192K across 244 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 10,827 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Akina, Inc., 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 1,689 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($268K) sits 22% above the category median. Disclosed range: $241K to $295K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Akina, Inc. AI Hiring
Akina, Inc. has 6 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Columbia, MD, US, Fort Meade, MD, US, Annapolis Junction, MD, US. Compensation range: $207K - $296K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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