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Senior Software Engineer
JOB CODE
TEC3009
JOB PROFILE NAME (Title)
Senior Software Engineer
MANAGEMENT LEVEL
7 Manager
DISCIPLINE
Software Engineering
REPORTS TO
Engineering Leadership
FSLA CLASSIFICATION
Exempt
The Senior Software Engineer designs, builds, and maintains scalable software solutions, guiding technical decisions and mentoring teammates. This role delivers high\-quality code, reviews designs, and improves system reliability and performance. The engineer collaborates across product, QA, and operations to plan releases, automate workflows, and resolve complex issues. Responsibilities include establishing coding standards, performing root\-cause analysis, and driving continuous improvement. The position supports roadmap execution, provides technical leadership within a team, and ensures secure, maintainable implementations aligned to business goals. Senior Software Engineer
Location: Remote
The Senior Software Engineer designs, builds, and maintains scalable software solutions, guiding technical decisions and mentoring teammates. This role delivers high\-quality code, reviews designs, and improves system reliability and performance. The engineer collaborates across product, QA, and operations to plan releases, automate workflows, and resolve complex issues. Responsibilities include establishing coding standards, performing root\-cause analysis, and driving continuous improvement. The position supports roadmap execution, provides technical leadership within a team, and ensures secure, maintainable implementations aligned to business goals.
Essential Job Skills/Duties
- Design and implement scalable backend services.
- Review code and coach engineers.
- Own technical delivery for projects.
- Drive architecture and standards adoption.
- Investigate, triage, and resolve defects.
- Automate build, test, and deployment pipelines.
Required Technical Skills
- Application Packaging: Packaging software applications with required dependencies and configurations into standardized formats for installation, deployment, and management across operating systems and environments.
- Application Programming Interfaces (APIs): Designing and implementing APIs that enable communication, integration, and data exchange between systems and platforms.
- CI/CD: Designing workflows and pipelines that support continuous integration, automated testing, and continuous deployment.
- Code Review: Reviewing source code to identify defects, improve quality, and ensure alignment with development standards.
- DevOps: Integrating software development and IT operations practices to enable continuous delivery, version control, feature management, application monitoring, and scalable system design.
- Security Engineering: Incorporating security controls and safeguards into systems to protect against operational disruptions and malicious threats.
- Test Automation: Developing and executing automated testing frameworks and scripts as part of the software testing lifecycle.
- Artificial Intelligence: Designing and developing systems that perform tasks requiring machine intelligence and autonomous decision\-making.
- Containerization: Deploying and managing applications within portable, isolated container environments.
- Cloud\-Agnostic Implementation: Designing workloads that operate across multiple cloud environments through appropriate abstraction and architecture patterns.
- Data Engineering: Designing and managing data infrastructure, pipelines, and processing systems to support analytics, reporting, and machine learning initiatives.
- Full\-Stack Development: Building and operating end\-to\-end applications across user interface, APIs, data storage, CI/CD, and cloud infrastructure.
- Systems Design: Defining system architecture, modules, interfaces, and data structures to meet functional and technical requirements.
Required Soft/Leadership Skills
- Clear, proactive communication.
- Collaborative cross\-functional teamwork.
- Mentorship and knowledge sharing.
- Structured problem solving under pressure.
- Ownership and accountability mindset.
Required Education \& Experience
- Bachelor's degree in Computer Science or related.
- Seven or more years software engineering.
- Experience leading technical initiatives.
- Track record delivering production systems.
Preferred Education \& Experience
- Master's degree in a technical field.
- Experience with distributed systems.
- Background in security best practices.
Preferred Licenses/Certifications
- Cloud architect or developer certification.
Supervisory Responsibilities
- Provides technical guidance to engineers.
- May lead small project teams.
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 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Asurion, 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 in Demand for This Role
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 $219,250 based on 424 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Asurion AI Hiring
Asurion has 3 open AI roles right now. They're hiring across AI Software Engineer, AI Agent Developer. Positions span Remote, US, San Mateo, CA, US, Nashville, TN, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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