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About This Role
About Us
UJET leads the way in AI\-powered contact center innovation, delivering a future\-proof, cloud platform that redefines the customer experience with cutting\-edge AI, true multimodality, and a mobile\-first approach. We infuse AI across every aspect of your customer journey and contact center operations, to drive automation and efficiency. UJET's AI solutions empower agents, optimize customer journeys, and transform contact center operations for elevated experiences and actionable insights. Built on a cloud\-native architecture with a unique CRM\-first approach, UJET ensures unmatched security, scalability, and prioritized data insights (without storing PII). Designed for effortless use, UJET partners with businesses to deliver exceptional interactions, smarter decision\-making, and accelerated growth in the AI\-driven world.
Learn more at www.ujet.cx.
Opportunity:
We're looking for a AI Software Engineer to help build the next generation of UJET's AI products: Spiral and AXO. This is a high\-impact role for someone who thrives in ambiguity, enjoys moving across the stack, and wants to turn cutting\-edge model capabilities into real\-world product experiences.
You'll work across backend systems, AI services, product features, APIs, and infrastructure. One day you might be building agent workflows in Python, the next shipping customer\-facing features in TypeScript, improving evals and reliability, or designing systems that process large\-scale conversational data securely and efficiently.
This role is ideal for an engineer who is deeply practical about AI: someone who uses AI\-assisted development naturally, understands how to build with and around LLMs, and cares about reliability, performance, and user impact as much as model quality. We value curiosity highly. The best people in this role are excited to dig into messy problems, ask good questions, and iterate until the system is measurably better.
Responsibilities:
- Build and own product and platform capabilities across Spiral and AXO, from early prototypes to production systems.
- Design and implement AI\-powered workflows, agent capabilities, and backend services that are scalable, secure, and reliable.
- Develop high\-performance APIs, async workers, and application logic in Python and TypeScript.
- Ship user\-facing product features and internal tools that make advanced AI systems useful and intuitive for customers.
- Architect and improve data ingestion, parsing, and analysis pipelines that transform raw customer interaction data into structured, actionable insights.
- Partner across engineering, product, and design to translate ambiguous product ideas into robust technical systems.
- Own infrastructure and deployment patterns on AWS/GCP, with a focus on reproducibility, observability, security, and cost efficiency.
- Improve system quality through evaluation, monitoring, logging, alerting, and operational best practices.
- Help define engineering standards, review code, and mentor teammates working on distributed systems and AI applications.
Requirements:
- 3\-5 years of professional software engineering experience, with strong hands\-on experience building production systems.
- Strong experience as a generalist engineer who can move fluidly between backend systems, frontend, AI services, APIs, and product development.
- Excellent programming skills in Python and TypeScript.
- Experience shipping AI agents, LLM\-powered applications, or other production AI systems, including prompt and tool orchestration, evaluation, and cost/reliability tradeoffs.
- Strong product instincts and comfort working in ambiguous environments where the right solution is not obvious at the start.
- Experience building and operating systems on AWS/GCP in production.
- Comfort working with large, messy datasets and building pipelines that turn unstructured inputs into dependable product functionality.
- Strong SQL and data systems fundamentals.
- A bias toward ownership, speed, and pragmatic execution.
- Experience building AI\-native products from 0 to 1\.
- Familiarity with conversational data, support platforms, CRM/CCaaS integrations, or customer experience tooling.
- Experience with observability, evaluation frameworks, and production reliability for AI systems.
Annual US Hiring Range: $150,000 \- $165,000
- *A candidate's actual placement within this range will depend on geographic location, work experience, education, and/or skill level.*
*\#LI\-Remote*
*\#LI\-Hybrid*
UJET is an Equal Opportunity Employer
UJET provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
Compliance Responsibilities
Security, data protection and compliance (SDPC) are paramount to the success of our partnerships. All roles at UJET require compliance with legal and regulatory requirements and acceptance and adherence to all policies and standards within UJET. Personnel acknowledges they are personally responsible for reporting any suspected violations or abuse and are required to complete SDPC training and fulfill role\-specific SDPC responsibilities.
Why UJET?
- Impactful Work: Be at the forefront of innovation, directly shaping the future of customer experience.
- Dynamic Culture: Join a collaborative, inclusive team that values big ideas, creative solutions, and powerful relationships.
- Comprehensive Benefits: Medical, dental, vision, 401(k) plan, commuter benefits, and more.
Salary Context
This $150K-$165K range is below the median for AI Software Engineer roles in our dataset (median: $190K across 219 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 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At UJET.cx, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($157K) sits 32% below the category median. Disclosed range: $150K to $165K.
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.
UJET.cx AI Hiring
UJET.cx has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Los Angeles, CA, US. Compensation range: $165K - $165K.
Location Context
AI roles in Los Angeles pay a median of $191,580 across 1,792 tracked positions. That's 4% below the national 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
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