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 Vice President of Data Science, you will lead and grow our in\-house data science team. This team is responsible for research, experimentation, data collection and curation, and data analysis that contributes to the performance of Five9's AI products. Tasks include evaluation of and selection of AI agent architectural frameworks, evaluation and comparison of LLM models across commercial and open source choices, model fine\-tuning for dedicated tasks, prompt engineering, prompt structure and design, and composite model definitions and evaluations. The scale of Five9 provides a wealth of data that data science team has access to. The data science team is very much applied – their work directly makes its way into real products providing direct customer benefit.
As lead of this team, you will take complete ownership of the technical and operational direction of the organization, including growing to team to meet increased demand for its capabilities.
Key Responsibilities:
- Technical Direction Setting: As an expert in the leading edge of AI and data science, you will direct the team on the methodologies, practices, algorithms, experiments and processes they perform.
- Hands On: You are expected to also be hands on, not just a manger, and be directly responsible for some amount of the technical work in addition to directing the team.
- Organizational Growth: You will be tasked with growing the team, and ensuring we have the right talent to accomplish our goals.
- Collaborator and Spokesperson: You will act as an internal and external spokesperson for data science, and collaborate with stakeholders across the company. Internally, you will be expected to meet with product managers, executives and estaff, and be able to converse effectively with them. You will also occasionally meet with customers to understand how Five9 products, and the data science behind them, impacts the customers. You are expected to participate in industry activities, including publication of blog posts and papers, along with participation in AI conferences.
Technical Expertise:
- 12\+ years experience in data science or AI applied research, ideally at a best\-in\-class applied research organization.
- Deep Expertise in Modern AI/ML
- + Extensive hands\-on experience with LLMs, agentic architectures, retrieval\-augmented systems, transformers, and composite model pipelines.
+ Strong understanding of commercial and open\-source model ecosystems (e.g., OpenAI, Anthropic, Google, Meta, Mistral), including evaluation, benchmarking, and tradeoff analysis.
- Model Development \& Optimization
- + Proven ability to perform fine\-tuning, supervised/unsupervised training, prompt engineering, prompt optimization, and model orchestration for real\-world use cases.
+ Experience designing evaluation frameworks, experiment methodologies, and robust model comparison workflows.
- Data Engineering \& Curation
- + Expertise in large\-scale data collection, labeling, cleaning, and curation pipelines, preferably with conversational or unstructured text data.
+ Familiarity with tools and techniques for data quality assessment, dataset versioning, and data governance.
- Applied Data Science \& Analytics
- + Strong proficiency in statistical analysis, A/B experimentation, causal inference, and performance measurement.
+ Demonstrated success turning data insights into product improvements that drive measurable business outcomes.
- Software Development \& Systems Thinking
- + Ability to work with engineering teams using modern software practices (Python, data platforms, cloud\-native environments, APIs, ML Ops tooling).
+ Understanding of production ML systems, deployment patterns, monitoring, and safety/guardrail design.
People \& Collaboration Skills:
- Cross\-Functional Partnering
- + Ability to collaborate effectively with product managers, engineering leaders, UX, and GTM teams to translate business needs into data science strategies.
+ Adept at explaining complex technical concepts to executives, customers, and non\-technical stakeholders.
- Communication \& Storytelling
- + Exceptional written and verbal communication skills, including ability to publish thought leadership (papers, blog posts) and present at conferences.
- Team Development \& Mentorship
- + Passion for mentoring senior and junior data scientists, fostering technical excellence, and building a culture of experimentation and rigorous thinking.
- Customer Empathy
- + Experience engaging directly with customers to understand their needs, gather feedback, and translate insights into product or model improvements.
Leadership \& Strategic Skills:
- Vision Setting \& Direction
- + Ability to define the data science strategy for AI Agents and customer experience products, aligning with corporate priorities and market opportunities.
- Hands\-On Leadership
- + Comfortable being an active contributor—writing code, running experiments, reviewing research—while simultaneously guiding the team's overall direction.
- Organizational Scaling
- + Experience hiring, scaling, and structuring high\-performing data science teams across multiple geographies.
- Operational Excellence
- + Ability to build processes for experimentation, model evaluation, data quality management, and continuous delivery of data science innovation into product.
- Executive Presence \& Influence
- + Skilled at influencing E\-staff and senior leadership, defending technical decisions, shaping product strategy, and representing data science internally and externally.
- Ethics, Safety \& Risk Awareness
- + Deep understanding of responsible AI principles, privacy considerations, and model safety, including evaluating risks when operating at enterprise scale.
Educational Requirements:
Advanced degree in a quantitative or technical field, such as:
- Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Applied Mathematics, Electrical Engineering, Computational Linguistics, or a related field.
- Master's degree in one of the above fields with significant applied industry experience in AI/ML leadership roles.
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.