Interested in this AI/ML Engineer role at Five9?
Apply Now →Skills & Technologies
About This Role
Join us in bringing joy to customer experience. Five9 is a leading provider of cloud contact center software, bringing the power of cloud innovation to customers worldwide.
Living our values everyday results in our team\-first culture and enables us to innovate, grow, and thrive while enjoying the journey together. We celebrate diversity and foster an inclusive environment, empowering our employees to be their authentic selves.
As VP of Engineering, AI Innovations, you will lead the our team of talented software developers that are responsible for Five9's AI Insights, Agent Assist and GenAI Studio products. These products are in widespread usage today. You will be responsible for directing the next phase of the evolution of these products, significantly expanding their feature sets, improving their capabilities with the latest and greatest in AI technologies, and expanding their reach to more customers with better scale and enterprise capability. You'll take end\-to\-end ownership, from development through operations.
Required Work Experience:
- 10\+ years experience in engineering management.
- Experience leading Engineering delivery for Customer Experience AI products, including Agent Assistance, AI Agents, and Conversational Analytics.
Required Skills
- Technical Skills
AI / ML / GenAI Expertise
- Deep understanding of modern AI architectures: LLMs, RAG systems, embeddings, vector databases, multimodal models.
- Practical experience integrating LLMs into production systems, including prompt orchestration, function calling, structured outputs, and multi\-agent workflows.
- Familiarity with model evaluation, A/B testing, safety techniques, latency/throughput trade\-offs, and observability for AI systems.
- Understanding of fine\-tuning, distillation, and model optimization (quantization, pruning, MoE, etc.).
- Experience with applied ML for NLP, ASR/TTS, NLU, and agent\-assist use cases preferred.
Software Engineering Foundations
- Expert\-level proficiency in Java and JVM\-based ecosystems.
- Strong command of modern distributed systems design: microservices, event\-driven architectures, concurrency, and fault tolerance.
- Experience in development and operations of software in public cloud (AWS, GCP, Azure). Nice\-to\-have: experience with Google Cloud Platform (GCP)
- Experience building highly scalable, low\-latency systems for enterprise SaaS.
- Strong understanding of API design (REST, gRPC), SDKs, and integrations with enterprise systems.
- Proficiency with CI/CD (GitHub Actions, Jenkins, Spinnaker, etc.) and Infrastructure as Code (Terraform).
Operational Excellence
- Deep knowledge of SRE principles: SLIs/SLOs, incident management, error budgets.
- Experience running 24×7 production systems at scale, ideally in multi\-region global deployments.
- Expertise in security, privacy, and compliance relevant to CCaaS environments (SOC2, GDPR, HIPAA, FedRAMP preferred).
- Solid grasp of network fundamentals (TCP/IP, HTTP/2/3, WebRTC basics, load balancing).
- People \& Leadership Skills
Engineering Leadership
- Proven ability to lead, mentor, and grow high\-performing engineering teams across multiple disciplines (backend, ML, frontend, DevOps, SRE).
- Track record of hiring top\-tier engineering leaders and technologists.
- Ability to create a culture of excellence—high velocity, high quality, and accountability.
- Strong conflict\-resolution skills; able to navigate ambiguity and align cross\-functional teams.
Collaboration \& Communication
- Exceptional written and verbal communication skills; able to clearly articulate vision, architecture, and trade\-offs to both technical and non\-technical audiences.
- Comfortable partnering with Product, Design, Sales Engineering, Customer Success, and Executive Leadership.
- Ability to inspire teams with a compelling technical vision and roadmap.
Customer\-Centric Mindset
- Deep empathy for customers and agents using Five9's AI products.
- Experience engaging customers directly to gather insights and validate direction.
- Ability to translate customer problems into clear technical strategies and roadmaps.
- Organizational \& Strategic Skills
Vision and Strategy
- Ability to define and execute the long\-range engineering strategy.
- Strong understanding of the AI competitive landscape; able to guide build\-vs\-buy decisions and partner evaluations.
- Experience driving architectural modernization initiatives to scale AI products.
Execution \& Delivery
- Mastery of engineering execution frameworks—OKRs, agile methodologies, metrics\-driven management.
- Ability to balance innovation with reliability, velocity, and long\-term maintainability.
- Skilled in managing large, multi\-quarter programs and cross\-org dependencies.
Budgeting and Resource Planning
- Proficiency with capacity planning, headcount allocation, staffing strategy, and forecasting infrastructure costs.
- Experience negotiating vendor contracts, including AI model providers and cloud services.
Change Management
- Ability to lead through rapid growth, shifts in technology, and organizational restructuring.
- Experience merging teams, establishing new engineering sites, and building distributed engineering organizations.
Five9 embraces diversity and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better we are. Five9 is an equal opportunity employer.
View our privacy policy, including our privacy notice to California residents here: https://www.five9\.com/pt\-pt/legal.
Note: Five9 will never request that an applicant send money as a prerequisite for commencing employment with Five9\.
Salary Context
This $233K-$512K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Five9, 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 $178,940 based on 11,900 positions with disclosed compensation. This role's midpoint ($372K) sits 108% above the category median. Disclosed range: $233K to $512K.
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 ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Five9 AI Hiring
Five9 has 4 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer. Positions span San Francisco, CA, US, San Ramon, CA, US, Remote, US. Compensation range: $218K - $512K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 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 ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.