Interested in this AI/ML Engineer role at Genesys?
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locations North Carolina, USA United States time type Full time posted on Posted Yesterday job requisition id JR112697Be the one building AI-powered experiences where they matter most.
At Genesys, we help organizations create better customer experiences through AI-powered experience orchestration. Our platform connects people, systems, data and AI to help organizations deliver more personalized service, improve operational efficiency and build stronger customer relationships.
Help build, support and operate technology used by more than 8,000 organizations in over 100 countries – moving AI from possibility to production in real-world enterprise environments every day.
Role Overview
Voice is rapidly becoming one of the most important interfaces for AI-powered customer experiences. As AI agents become more capable, the quality of the experience will increasingly be defined not only by what an AI agent can do, but by how naturally, appropriately, and authentically it communicates.
Genesys is looking for a Director of Product Management, Voice AI & Speech Experience to own the strategy and execution of our AI voice technology portfolio across Genesys Cloud.
This leader will be responsible for making Genesys the benchmark for enterprise AI voice experiences. The role will own our OEM voice technology strategy, evaluate and manage leading voice technology partners, define the capabilities we bring into Genesys Cloud, and ensure customers have access to the right voices, languages, performance, and experiences for their brands.
The underlying technology may include traditional speech synthesis, generative voice models, speech-to-speech models, multimodal models, or emerging voice technologies. This leader will be responsible for evaluating these approaches based on the customer experience they enable, rather than being tied to a specific technical architecture.
A key part of the role will be serving as the Genesys Voice Experience Designer, working directly with customers and our Go-to-Market teams to help organizations design the right voice experience for their brand. This includes helping customers translate their brand identity, customer demographics, use cases, and desired emotional experience into the right voice, persona, conversational style, and technology approach.
This is a highly cross-functional Director-level role requiring strong product judgment, technical understanding of modern voice AI, customer-facing credibility, commercial awareness, and the ability to bring Product, Engineering, Design, Sales, Solutions Consulting, Marketing, partners, and customers together around a common vision for voice.
Key Responsibilities
Own the Genesys Voice AI Strategy
- Define and own the product strategy and roadmap for AI voice capabilities across Genesys Cloud.
- Establish the vision and quality bar for what a best-in-class enterprise AI voice experience should sound and feel like.
- Evaluate emerging voice technologies, models, architectures, and providers and determine where Genesys should build, partner, OEM, or integrate.
- Develop a portfolio strategy that balances voice quality, latency, conversational intelligence, expressiveness, language coverage, regional availability, enterprise requirements, cost, and reliability.
- Evaluate traditional speech architectures alongside emerging speech-to-speech, generative audio, multimodal, and end-to-end conversational voice models.
- Partner closely with Engineering and AI teams to translate advances in speech and generative AI into production capabilities that customers can use at enterprise scale.
- Ensure the voice strategy supports the broader Genesys AI, Agentic AI, Virtual Agent, and conversational experience roadmap.
- Continuously assess how new voice architectures can improve latency, naturalness, turn-taking, interruption handling, prosody, emotional intelligence, and overall customer experience.
Own the OEM Voice Technology Ecosystem
- Own the product strategy and relationships with strategic voice AI technology providers.
- Continuously benchmark OEM technologies across naturalness, latency, prosody, expressiveness, pronunciation, interruption handling, conversational responsiveness, language and accent coverage, reliability, and enterprise readiness.
- Establish clear frameworks for evaluating strategic voice technology providers and determining the right technology for different customer and product use cases.
- Work with Engineering, Procurement, Legal, Security, Finance, and strategic partners to influence vendor roadmaps and make technology and commercial decisions.
- Determine when Genesys should use embedded OEM capabilities, customer-provided technology, strategic integrations, or native Genesys capabilities.
- Monitor the rapidly evolving voice AI market and identify emerging technologies, models, and partnerships that can create differentiated customer experiences.
- Help shape commercial models that allow Genesys to deliver premium voice experiences while maintaining scalable economics.
- Develop strong relationships with strategic technology partners and ensure Genesys requirements influence their product roadmaps.
Serve as the Genesys Voice Experience Sherpa
- Establish a customer-facing discipline around helping organizations design the right AI voice experience for their brand and customers.
- Work directly with strategic customers to understand their brand personality, customer demographics, desired emotional experience, industry, use cases, and geographic markets.
- Guide customers through the rapidly evolving voice AI landscape, including traditional speech architectures, generative voice, speech-to-speech, multimodal, and emerging voice technologies.
- Help customers select and shape voice experiences based on tone, warmth, confidence, pace, energy, accent, personality, emotional range, conversational style, and brand alignment.
- Advise customers on the tradeoffs between different voice technologies across experience quality, latency, control, language coverage, consistency, cost, and enterprise requirements.
- Help customers determine when to use standard voices, customized voices, branded voices, real-time generative voice models, or other emerging approaches.
- Help customers evaluate how voice choices affect trust, brand perception, customer engagement, containment, and overall satisfaction.
- Create repeatable workshops, evaluation frameworks, best practices, and methodologies that allow Genesys GTM teams and partners to deliver this guidance at scale.
- Partner with Design and Conversation Design teams to ensure voice, persona, language, and conversational behavior create a cohesive end-to-end experience.
- Develop showcase customer experiences that demonstrate what best-in-class AI voice interactions can feel like.
Drive the Best Customer Experience
- Treat voice as a core customer experience discipline, not simply a speech technology problem.
- Define success metrics that measure naturalness, latency, conversational fluidity, customer acceptance, engagement, task completion, satisfaction, and preference.
- Establish benchmarking and testing programs using real-world CX scenarios across different languages, accents, telephony environments, customer intents, and emotional situations.
- Evaluate the entire conversational experience, including response timing, turn-taking, interruptions, pacing, tone, pronunciation, emotional appropriateness, and recovery from conversational errors.
- Use customer research, production telemetry, competitive analysis, and direct customer feedback to continuously improve the experience.
- Understand how model behavior, latency, conversational pacing, prosody, voice personality, and turn-taking affect overall customer experience.
- Partner closely with the teams building Virtual Agents, Agentic AI, IVR, orchestration, and conversational experiences to ensure voice is designed as part of the end-to-end experience.
- Establish measurable quality standards and ensure Genesys consistently meets or exceeds them across its voice portfolio.
Partner Deeply with Go-to-Market Teams
- Work directly with Sales, Solutions Consulting, Customer Success, Professional Services, Marketing, and Product Marketing to make voice a differentiated part of the Genesys value proposition.
- Join strategic customer engagements where voice quality and AI experience are critical to winning, expanding, or retaining the relationship.
- Help GTM teams explain differences between voice technologies in terms customers can understand and connect those differences to customer experience and business outcomes.
- Build clear competitive positioning for Genesys voice capabilities and articulate where Genesys is differentiated.
- Create demonstrations, customer workshops, competitive positioning, playbooks, reference architectures, and enablement that allow the field to confidently sell Genesys voice capabilities.
- Partner with lighthouse customers to create differentiated voice experiences that demonstrate what is possible with Genesys Cloud.
- Help customers navigate a rapidly changing voice AI landscape and position Genesys as a trusted advisor rather than simply a provider of voice technology.
- Bring direct customer, field, and competitive insights back into the product roadmap.
- Partner with Product Marketing to translate product capabilities into compelling market narratives and launches.
Drive Product Execution
- Translate strategy into clear product requirements, priorities, and execution plans.
- Work closely with Engineering and Design to deliver voice capabilities that meet both customer needs and enterprise requirements.
- Define launch criteria, readiness standards, adoption goals, and success metrics for new capabilities.
- Prioritize across short-term customer needs, strategic platform investments, technical debt, and emerging market opportunities.
- Drive alignment across multiple teams when the customer experience spans different parts of the Genesys platform.
- Use data and customer feedback to make tradeoffs and adjust roadmap priorities quickly.
- Ensure roadmap decisions balance customer experience, technical feasibility, commercial value, operational complexity, and long-term strategic differentiation.
Cross-Functional Leadership
- Lead through a highly matrixed organization, setting direction and driving alignment without relying solely on formal authority.
- Partner closely with Product, Engineering, Design, Research, AI, Infrastructure, Finance, Legal, Procurement, Marketing, and GTM organizations.
- Influence product roadmaps and priorities beyond your direct area of ownership where they affect the end-to-end voice experience.
- Communicate technology choices, customer needs, competitive dynamics, tradeoffs, and recommendations clearly to senior leadership.
- Translate highly technical developments in voice AI into customer and business outcomes that can be understood across the organization.
- Build strong relationships across internal teams and external partners and create clarity in an area of technology that is changing rapidly.
Required Qualifications
- 10+ years of Product Management, Product Strategy, Experience Design, or related experience in enterprise SaaS, AI, communications, speech technology, or adjacent technologies.
- Proven leadership at a Director level or equivalent, including experience driving complex initiatives across multiple organizations.
- Strong understanding of modern voice AI, conversational AI, speech technologies, generative audio, or adjacent AI technologies.
- Understanding of emerging architectures including speech-to-speech, multimodal, and end-to-end voice models.
- Experience evaluating external technology providers and making build, buy, partner, or OEM product decisions.
- Strong technical aptitude and the ability to work credibly with Engineering and AI/ML teams on architecture, model performance, APIs, latency, scalability, reliability, and quality.
- Demonstrated ability to translate customer experience requirements into product strategy and execution.
- Strong customer-facing skills with experience working directly with enterprise customers and senior executives.
- Experience partnering with Sales, Solutions Consulting, Product Marketing, Customer Success, or other GTM functions.
- Strong commercial judgment and the ability to evaluate product decisions through both customer experience and business lenses.
- Ability to operate effectively in a rapidly changing technology market where architectures, models, and competitive expectations evolve quickly.
- Excellent written and verbal communication skills, with the ability to explain complex technical concepts clearly to both technical and non-technical audiences.
Preferred Qualifications
- Direct experience with voice AI platforms, speech AI providers, generative voice, speech-to-speech models, multimodal models, or conversational AI.
- Experience managing OEM technology partnerships or embedded third-party AI technologies.
- Experience benchmarking or evaluating AI models or speech technologies.
- Understanding of speech characteristics including prosody, pronunciation, pacing, intonation, emotional expression, accents, interruption handling, and conversational turn-taking.
- Experience designing conversational or voice experiences for customer service.
- Experience with CCaaS, contact center technology, Virtual Agents, IVR, or enterprise conversational AI.
- Experience launching AI products globally across multiple languages and regions.
- Familiarity with enterprise AI requirements including security, privacy, data residency, regulatory compliance, responsible AI, and accessibility.
- Experience developing commercial or pricing strategies for AI-powered capabilities.
#LI-Remote
Compensation:
This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate’s experience, qualifications, skills, and location. This role might also be eligible for a commission or performance-based bonus opportunities.
$193,700.00 - $340,700.00Benefits:
- Medical, Dental, and Vision Insurance.
- Telehealth coverage
- Flexible work schedules and work from home opportunities
- Development and career growth opportunities
- Open Time Off in addition to 10 paid holidays
- 401(k) matching program
- Adoption Assistance
- Fertility treatments
Click here to view a summary overview of our Benefits.
Working at Genesys
- AI at enterprise scale – Build, support and operate AI-powered technology used by more than 8,000 organizations worldwide. 150+ new AI features were released in the last fiscal year.
- A flexible-first culture – Join a global team of nearly 7,000 employees with flexible ways of working designed to help people do their best work.
- Growth in the AI era – Build future-ready skills through mentorship, learning programs, leadership development and education support.
- Time to recharge and give back – Benefits include paid volunteer time, August Free Fridays, well-being resources and regionally tailored programs for employees and their families.
- Recognized globally – Genesys is Great Place to Work® certified in 17 countries and 94% of employees are proud to tell others they work at Genesys.
Learn more about our culture, AI innovation and sustainability commitments through our Careers site and Sustainability Report.
What Happens After You Apply
After you apply, here's what you can typically expect:
- Our Talent Acquisition team reviews your application with the hiring team.
- A Talent Acquisition Partner will review your application and, if your background is aligned, schedule a Zoom interview.
- Next, you'll meet the hiring manager and other members of the interview team.
- We aim to keep the process focused and respectful of your time, with no more than five interviews in most cases.
- After interviews are complete, our team will follow up with the final steps.
Every application is reviewed by a person. Response times may vary by role and location, but our team will keep you informed throughout the process.
Stay Connected
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Employee Referral
If a Genesys employee referred you, please apply using the link they shared so we can connect your application to their referral.
About Genesys:
Genesys® empowers more than 8,000 organizations worldwide to create the best customer and employee experiences. Genesys Cloud™ is the Agentic Orchestration Platform that securely connects people, systems, data and AI across the enterprise with built-in governance and control. As a result, organizations can drive customer loyalty, growth and retention while increasing operational efficiency and teamwork across human and AI workforces. To learn more, visit www.genesys.ai.
Reasonable Accommodations:
If you require a reasonable accommodation to complete any part of the application process, or are limited in your ability to access or use this online application and need an alternative method for applying, you or someone you know may contact us at [email protected].
You can expect a response within 24–48 hours. To help us provide the best support, click the email link above to open a pre-filled message and complete the requested information before sending. If you have any questions, please include them in your email.
This email is intended to support job seekers requesting accommodations. Messages unrelated to accommodation—such as application follow-ups or resume submissions—may not receive a response.
Genesys is an equal opportunity employer committed to fairness in the workplace. We evaluate qualified applicants without regard to race, color, age, religion, sex, sexual orientation, gender identity or expression, marital status, domestic partner status, national origin, genetics, disability, military and veteran status, and other protected characteristics.
Please note that recruiters will never ask for sensitive personal or financial information during the application phase.
Salary Context
This $193K-$340K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Genesys, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($267K) sits 26% above the category median. Disclosed range: $193K to $340K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Genesys AI Hiring
Genesys has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span NC, US, Ontario, Canada, IN, US. Compensation range: $194K - $340K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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