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About This Role
Say hello to opportunities.
It’s not everyday that you consider starting a new career. We’re RingCentral, and we’re happy that someone as talented as you is considering this role. First, a little about us, we’re a $2 Billion annual revenue company with double digit Annual Recurring Revenue (ARR) and a $93 Billion market opportunity in UCaaS, Contact Center and AI\-powered adjacencies. We invest more than $250 million annually to ensure our AI\-enabled technology and platforms meet or exceed the needs of our customers.
RingSense AI is our proprietary AI solution. It’s designed to fit the business needs of our customers, orchestrated to be accurate and precise, and built on the same open platform principles we apply to our core software solutions.
RingCentral's Enterprise AI organization is looking for a strong hybrid: someone who can walk into a business meeting, identify the enterprise AI opportunity, architect the solution, and then build it. This is not a traditional product management role. We are hiring an AI Product Engineer — a hands\-on technologist with enough product instinct to own the “why,” enough architecture depth to define the “how,” and enough engineering skill to actually build it.
You will sit within our product management team and serve as a force multiplier — partnering with business stakeholders \& product managers to uncover AI opportunities, designing end\-to\-end solutions, prototyping quickly, and driving production delivery alongside engineering. The emphasis is firmly on building and architecting, with product management as your professional home base.Architect AI Solutions End\-to\-End* Join business meetings and workshops to uncover automation and AI opportunities in real time; translate them into concrete solution architectures on the spot.
- Design and recommend solution patterns — RAG pipelines, agentic workflows, MCP integrations, prompt/eval frameworks — choosing the right approach for each use case.
- Own build\-vs\-buy decisions: evaluate third\-party AI tools (Copilot, Gemini, Claude, etc.) against custom in\-house builds using structured technical and business criteria.
- Define data flows, integration points, and system contracts across enterprise platforms such as Salesforce, Workday, NetSuite, and cloud AI services (AWS, GCP, Azure).
Build and Ship Hands\-On* Develop working prototypes, proof\-of\-concepts, and production\-grade AI features — not slide decks.
- Implement and iterate on RAG pipelines, LLM orchestrations, agentic workflows, API integrations, and chatbot/copilot experiences.
- Establish telemetry and LLM evaluation frameworks (correctness, faithfulness, latency, cost, token usage) and monitor live systems post\-launch.
- Collaborate closely with engineering teams through code reviews, technical workshops, and paired development sessions.
Drive Product Direction* Partner with business units (Sales, Marketing, HR, Finance, Legal, CX) to identify high\-impact use cases, quantify ROI, and define measurable success criteria.
- Maintain the product roadmap for AI initiatives, owning quarterly planning, backlog prioritization, and end\-to\-end AI\-DLC from discovery through launch and iteration.
- Keep a current AI tool landscape and comparison matrix spanning general\-purpose copilots and function\-specific enterprise apps.
- Use agile rituals and rapid experimentation to learn quickly and keep delivery momentum.
Evangelize and Enable* Serve as the organization’s resident AI practitioner: educate stakeholders on what’s possible, set realistic expectations, and demystify technical concepts for non\-technical audiences.
- Lead AI training sessions, internal demos, and working sessions to accelerate adoption across business functions.
- Monitor and communicate the quantifiable impact of launched AI solutions (time saved, quality, adoption, CSAT).
Requirements* 3\+ years of hands\-on experience building or integrating AI\-powered solutions in an enterprise setting (RAG, LLM applications, AI agents, or similar).
- Demonstrated ability to architect solutions from scratch — designing data flows, integration patterns, and system components in live stakeholder settings.
- Proficiency in at least one scripting or development language (Python strongly preferred; familiarity with JavaScript/TypeScript a plus).
- Solid grasp of modern AI/ML concepts: LLMs, embeddings, vector databases, prompt engineering, retrieval\-augmented generation, and agent frameworks.
- Experience integrating with enterprise APIs and platforms (REST, GraphQL, OAuth 2\.0, webhooks) and familiarity with data stores (PostgreSQL, MongoDB, Redis, or similar).
- Strong communication skills — able to explain technical architecture clearly to executives, and to translate fuzzy business problems into precise technical requirements.
- A track record of shipping: prototypes that became products, pilots that became programs.
Nice to Have* Exposure to enterprise systems: Salesforce, Workday, NetSuite, ServiceNow, or similar HRIS/CRM/ITSM platforms.
- Experience with conversational AI platforms: Google Dialogflow CX, Microsoft Copilot Studio, or equivalent.
- Familiarity with cloud AI/ML services on AWS, GCP, or Azure.
- Background or coursework in product management, systems design, or solutions architecture.
- Comfort with SQL and analytical tooling for evaluating AI system performance.
This role is for the technology enthusiast who is equally energized by a whiteboard architecture session and an afternoon of building. You will have real ownership over meaningful problems, a short path from idea to production, and the support of a world\-class engineering organization — all from RingCentral’s Silicon Valley headquarters.What we offer:* Comprehensive medical, dental, vision, disability, life insurance
- Health Savings Account (HSA), Flexible Spending Account (FSAs) and Commuter benefits
- Voluntary supplemental health coverage and life insurance
- 401K match and ESPP
- Paid time off and paid sick leave
- Paid parental and pregnancy leave
- Family\-forming benefits (IVF, Preservation, Adoption etc.)
- Emergency backup care (Child/Adult/Pets)
- Employee Assistance Program (EAP) with counseling sessions available 24/7
- Free legal services that provide legal advice, document creation and estate planning
- Employee bonus referral program
- Student loan refinancing assistance
- Employee 1:1 coaching, perks and discounts program
RingCentral’s IT team ensures company data is accessible, secure, and optimized in ways that provide maximum competitive advantage. We are constantly discovering, developing and deploying innovations that power productivity and drive better decisions for our customers. Our IT professionals are talented, ambitious, out\-of\-the\-box thinkers who love to learn on the job—planning, deploying and maintaining state\-of\-the\-art technology to deliver flawless performance 24/7/365\.
RingCentral’s work culture is the backbone of our success. And don’t just take our word for it: we are recognized as a Best Place to Work by Glassdoor, the Top Work Culture by Comparably and hold local BPTW awards in every major location. Bottom line: We are committed to hiring and retaining great people because we know you power our success.About RingCentral
RingCentral, Inc. (NYSE: RNG) is a leading provider of business cloud communications and contact center solutions based on its powerful Message Video Phone™ (MVP™) global platform. More flexible and cost effective than legacy on\-premises PBX and video conferencing systems that it replaces, RingCentral® empowers modern mobile and distributed workforces to communicate, collaborate, and connect via any mode, any device, and any location. RingCentral is headquartered in Belmont, California, and has offices around the world.
RingCentral is an equal opportunity employer that truly values diversity. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to providing reasonable accommodations for individuals with disabilities during our application and interview process. If you require such accommodations, please click on the following link to learn more about how we can assist you.
If you are hired in Belmont, California, the compensation range for this position is between $127,400 and $182,000 for full\-time employees, in addition to eligibility for variable pay, equity, and benefits. Benefits may include, but are not limited to, health and wellness, 401k, ESPP, vacation, parental leave, and more! The salary may vary depending on your location, skills, and experience.
Salary Context
This $127K-$182K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At RingCentral, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($154K) sits 7% below the category median. Disclosed range: $127K to $182K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
RingCentral AI Hiring
RingCentral has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Agent Developer. Positions span Remote, US, Belmont, CA, US. Compensation range: $182K - $215K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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