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About This Role
Say hello to possibilities.
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 the $2 billion global leader in cloud\-based communications and collaboration software. We are fundamentally changing the nature of human interaction— giving people the freedom to connect powerfully and personally from anywhere, at any time, on any device.
This is where you and your skills come in. We’re currently looking for: Responsible to support the design, implementation, and optimization of scalable Artificial Intelligence (“AI”) and Machine Learning (“ML”) solutions.
To succeed in this role you must meet the following requirements:
- Collaborate with cross\-functional teams (Product, Data, Engineering) to develop and implement AI/ML solutions
- Establish and promote ML Operations and lifecycle management best practices
- Design, build, and manage scalable ML infrastructure
- Develop, validate, deploy, and monitor ML models and applications
- Create and maintain optimized data pipelines
- Utilize programming languages (Python, R, Scala, Java) and adopt CI/CD best practices, including automation and monitoring
- Implement state\-of\-the\-art Generative AI models and workflows (ASR, LLM, TTS, RAG, \& Search)
- Stay up to date with emerging ML/AI technologies, frameworks, and best practices
- Scale and support large distributed systems serving millions of customers daily
- Maintain and extend RingCentral’s internal AI platform
- Build Agentic AI products to drive business value
- Bring thought leadership to AI products \& services
Desired Qualifications:
- U.S. Master’s degree in Data Science, Computer Engineering or a related field, or foreign equivalent, plus two (2\) years of related experience, or U.S. Bachelor’s degree or equivalent in Data Science, Computer Engineering or a related field plus five (5\) years of related experience, is required.
- Experience with analytics, Machine Learning (“ML”), data science, platform development, data aggregation and analysis, statistical analysis, ML/AI modeling, TensorFlow, PyTorch, Generative AI, NLP, deep learning, statistical methods, GCP, AWS, Azure ML, data structures, algorithms, software architecture, MLOps, ML lifecycle management, CI/CD, and Docker is required.
What we offer:
- Comprehensive medical, dental, vision, disability, life insurance
- Health Savings Account (HSA), Flexible Spending Account (FSAs) and Commuter benefits
- 401K match and ESPP
- Paid time off and paid sick leave
- Wellness programs including 1:1 coaching and meditation guidance
- Paid parental and pregnancy leave and new parent gift boxes
- Family\-forming benefits (IVF, Preservation, Adoption etc.)
- Emergency backup care (Child/Adult/Pets)
- Parental support for children with developmental and learning disabilities
- Pet insurance
- 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 perks and discounts program
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. RingCentral offers on\-site, remote and hybrid work options optimized for the ways we work and live now.
About RingCentral
RingCentral, Inc. (NYSE: RNG) is a leading provider of business cloud communications and contact center solutions based on its powerful Message Video PhoneTM (MVPTM) 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.
This posting is pursuant to and is in compliance with the applicable federal rules of the U.S. Department of Labor regulations. 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. We hire for this role frequently. There is no application deadline for this role.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
RingCentral AI Hiring
RingCentral has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Belmont, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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