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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.
Sr AI\-Driven Enterprise Support Engineer
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Location: Seattle or Hybrid Preferred https://www.nice.com/company/global\-locations (Remote Considered)
The Future of Enterprise Support Starts Here
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What if support wasn't measured by how many tickets you close, but by the outcomes you create?
At NICE, we're building the next generation of enterprise customer support—where AI handles routine work, and exceptional engineers focus on what humans do best: solving complex problems, building customer trust, and driving strategic outcomes.
This isn't a traditional support engineering role.
It's an opportunity to become one of the first members of a team redefining how enterprise software support operates in the age of AI.
You'll partner with some of the world's most recognizable brands, leverage cutting\-edge AI tools built specifically for support operations, and serve as the technical quarterback for a portfolio of enterprise customers running mission\-critical contact center technology.
If you're equally comfortable troubleshooting a complex technical issue and leading a strategic customer conversation, we want to talk.
Why This Role Is Different
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Most support organizations ask engineers to manage tickets.
We're asking you to manage outcomes.
You'll leverage AI\-powered assistants that surface insights, identify patterns, summarize account activity, and provide technical context—allowing you to focus on relationship building, critical thinking, and technical leadership.
Rather than sitting in a reactive queue, you'll own customer success from a support perspective, combining deep technical expertise with strategic account ownership.
You'll help shape a role that doesn't exist in most organizations today.
What You'll Do
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### Own Enterprise Customer Relationships
- Serve as the primary technical support partner for a portfolio of enterprise customers
- Build trusted relationships with technical and operational stakeholders
- Conduct account reviews, identify trends, and proactively address risks
- Help customers maximize the value of their NICE investments
- Partner closely with Customer Success, Services, Product, and Engineering teams
### Solve Complex Technical Problems
- Troubleshoot advanced issues across NICE CXone and related applications
- Investigate routing, telephony, analytics, integrations, APIs, and platform performance
- Act as the quarterback for escalations, coordinating SMEs and Engineering teams when necessary
- Drive issues to resolution while maintaining exceptional customer communication
- Translate technical complexity into clear business outcomes
### Leverage AI as a Force Multiplier
- Utilize AI\-driven tools to accelerate investigations and customer insights
- Review AI\-generated recommendations and apply technical judgment
- Provide feedback that helps improve support automation capabilities
- Use AI\-powered account intelligence to identify opportunities and risks proactively
- Help establish best practices for AI\-augmented customer support
### Influence the Future
- Share customer feedback directly with Product and AI teams
- Help define how emerging AI support capabilities are used at enterprise scale
- Contribute to knowledge management and support process innovation
- Mentor peers and promote operational excellence
What We're Looking For
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### Required Experience
- 6\+ years supporting enterprise SaaS, cloud, CCaaS, or contact center technologies
- Experience owning customer\-facing technical relationships
- Strong troubleshooting and problem\-solving capabilities
- Experience managing complex escalations and cross\-functional resolution efforts
- Familiarity with APIs, integrations, logs, and platform diagnostics
- Ability to communicate effectively with both technical and business stakeholders
### Preferred Experience
- NICE CXone, Genesys Cloud, Cisco, Five9, Avaya, Amazon Connect, or similar platforms
- Technical Account Management (TAM) experience
- Contact center technologies including ACD, IVR, omnichannel routing, and workforce solutions
- Salesforce, ServiceNow, Dynamics 365, or CRM integrations
- Experience leveraging AI tools in technical support or customer\-facing environments
You'll Thrive Here If You...
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- Love solving difficult customer problems
- Enjoy building trusted relationships with enterprise stakeholders
- See AI as a tool that amplifies your effectiveness
- Want ownership instead of a ticket queue
- Are excited to help create something new rather than inherit something established
Why NiCE
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You'll join a highly supportive leadership team known for investing in people, collaboration, and growth. You'll work alongside experienced technical leaders while partnering with some of the biggest brands in the world.
Most importantly, you'll help build a new operating model for enterprise support at a company leading the conversation around AI\-powered customer experience.
*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/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At NiCE, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 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.
NiCE AI Hiring
NiCE has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Sandy, UT, US, Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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