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About This Role
At NiCE, we don't limit our challenges. We challenge our limits. Always. We're ambitious. We're game changers. And we play to win. We set the highest standards and execute beyond them. And if you're like us, we can offer you the ultimate career opportunity that will light a fire within you.
ABOUT NICE
NiCE is a global leader in cloud platforms for customer experience and contact center technology, serving over 25,000 organizations worldwide. We help businesses deliver smarter, faster, and more human interactions — at scale. Our engineering teams are at the center of that mission, building the products and platforms that transform how companies engage with their customers.
THE ROLE
We're looking for a technically strong engineer who is genuinely excited about AI — not because it's a trend, but because they've already been using it to do better work and they want to go further. This person doesn't wait for training programs or permission to level up. They experiment on their own, stay ahead of what's available, and bring what they learn back to the team.
You have real engineering depth — you know how to design systems, write code that holds up in production, and solve hard problems. You also have real AI experience: you've worked with LLMs and AI tools in ways that go beyond surface\-level prompting. You understand the trade\-offs, you've seen where these tools break down, and you've figured out how to get the most out of them anyway.
What sets you apart is the hunger. You're the kind of engineer who reads the release notes, follows what's shipping in the AI space, and finds ways to apply it before anyone asks you to. You want to be at the front of where this is going — and you're willing to put in the work to get there.
WHAT YOU'LL DO
- Design, build, test, and ship software across the full development lifecycle
- Use AI coding tools and Agentic AI Workflows actively in your day\-to\-day work — for code generation, review, testing, debugging, and documentation
- Write clean, well\-structured, Code and/or specification docs, leveraging AI as a partner, and also take responsibility for its quality in production. Take responsibility for its quality in production
- Collaborate with product managers, designers, and other engineers to understand requirements and deliver working solutions
- Apply prompt engineering and Agentic coding techniques to accelerate your work and improve output consistency
- Participate in code reviews, technical discussions, and team planning
- Evaluate and adopt new AI tools and approaches as the landscape evolves
- Contribute to shared engineering standards and help raise the bar for how the team uses AI
WHAT WE'RE LOOKING FOR
Engineering Fundamentals
- Solid experience in backend, frontend, or full\-stack software development
- Proficiency in one or more modern programming languages and frameworks
- Comfortable working with APIs, distributed systems, and software integrations
- Strong understanding of testing practices, code quality, and software design principles
AI \& LLM Experience
- Hands\-on experience using AI coding tools in real development work — not just exploration or hobby projects
- Practical knowledge of how LLMs work, where they're effective, and where they fall short
- Experience with prompt engineering — writing, iterating, and evaluating prompts for reliable output
- Familiarity with agents, tool use, or AI\-assisted workflow automation
- Working understanding of LLM constraints: context windows, token costs, latency, and output variability
Communication \& Collaboration
- Communicates clearly in both technical and non\-technical conversations
- Writes documentation, specs, and code comments that others can actually use
- Works well in cross\-functional teams and gives constructive code review feedback
- Takes ownership end\-to\-end — not just the assigned task, but the outcome
NICE TO HAVE
- Experience in CX, contact center, or enterprise SaaS environments
- Familiarity with CCaaS platforms, voice/chat AI, or customer\-facing AI products
- Exposure to building or tuning agentic systems or multi\-step AI pipelines
- Experience contributing to shared tooling, internal platforms, or developer experience improvements
WHAT TO EXPECT AT NICE
- A team that treats AI fluency as a professional standard — you won't be the only one who takes this seriously
- Access to modern AI tools, frameworks, and the space to experiment and improve how you work
- Real ownership — you'll be trusted to make decisions and see your work through to production
- A culture of continuous improvement, where how we build is as important as what we build
- Clear paths to grow into specialized engineering roles as your skills and interests develop
READY TO APPLY?
If you're a strong engineer who uses AI as a genuine part of how you work — not as a buzzword — we'd like to hear from you. We care more about what you've built and how you think than where you went to school or what your title was.
NiCE is an equal opportunity employer. We celebrate diversity and are committed to building an inclusive environment for all employees.
*About NiCE*
*NICE Ltd. (NASDAQ: NICE) software products are used by 25,000\+ global businesses, including 85 of the Fortune 100 corporations, to deliver extraordinary customer experiences, fight financial crime and ensure public safety. Every day, NiCE software manages more than 120 million customer interactions and monitors 3\+ billion financial transactions.*
*Known as an innovation powerhouse that excels in AI, cloud and digital, NiCE is consistently recognized as the market leader in its domains, with over 8,500 employees across 30\+ countries.*
*NiCE is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, age, sex, marital status, ancestry, neurotype, physical or mental disability, veteran status, gender identity, sexual orientation or any other category protected by law.*
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 4,021 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At NiCE, 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 $234,000 based on 758 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 $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
NiCE AI Hiring
NiCE has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Hoboken, NJ, US, Remote, US, Sandy, UT, US.
Location Context
Across all AI roles, 15% (608 positions) offer remote work, while 3,392 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,102 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 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 4,021 open positions tracked in our dataset. By seniority: 118 entry-level, 1,906 mid-level, 1,555 senior, and 442 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (608 positions). The remaining 3,392 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 4,021 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,818), Data Scientist (312), AI Software Engineer (280). 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 (118) are outnumbered by mid-level (1,906) and senior (1,555) 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 442 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (608 positions), with 3,392 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 $200,000. Top-quartile roles start at $253,000, 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 $290,000 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 (2,069 postings), Aws (1,260 postings), Azure (946 postings), Rag (893 postings), Gcp (783 postings), Pytorch (624 postings), Prompt Engineering (619 postings), Claude (570 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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