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About This Role
Say hello to opportunities.
If you’re looking to be part of what’s next in communication, you’re in the right place.
At RingCentral, we believe the best customer experiences happen when humans and AI work together. Our agentic voice AI portfolio—AIR, AVA, and ACE—brings together automation, assistance, and insights across the entire conversation lifecycle. The result? More seamless, intelligent experiences for businesses everywhere.
With $2\.5B\+ in ARR and $250M invested in R\&D annually, we’re building the future of AI\-powered business communications.
RingCentral is seeking a Director of Legal Innovation \& AI Enablement to lead the next phase of our legal department's evolution. The focus of this role is our sell\-side and buy\-side contracting processes — making them smarter, faster, and easier for everyone involved by leveraging AI and deeper integration with our commercial systems.
On the sell side, this means transforming how Sales and sales desk create, negotiate, and manage contracts — enabling greater autonomy through AI\-assisted intake, contract composition, redline review, and negotiation support that is natively integrated with our CRM and Quote\-to\-Cash workflows. On the buy side, it means applying the same rigor to vendor and procurement contracting. The result should be a more seamless, intelligent contracting experience across the full commercial lifecycle.
This is a program ownership role. The Director will develop a thorough understanding of RingCentral's current contracting processes, help define the vision for how AI can enhance them, and drive that vision to completion across Legal, Sales, Finance, Engineering, and IT. The expectation is full ownership — of the strategy, the execution, and the outcomes.
The ideal candidate has current, hands\-on knowledge of the best AI tools available for contract drafting, review, and negotiation, and the judgment to know which to deploy and how to integrate them effectively. Where commercial solutions fall short, they will identify opportunities to build custom in\-house AI agents in partnership with internal organizations
This role is based on\-site at RingCentral's Belmont headquarters. Driving cross\-functional programs of this scope requires close collaboration and daily presence with the teams involved.
Our current stack includes DocuSign CLM, Salesforce (QTC), Coupa, and NetSuite. We are actively evaluating next\-generation AI contracting platforms and expect this person to lead that evaluation and transition.
Key Responsibilities
Sell\-Side \& Buy\-Side Contract Transformation
- Own the end\-to\-end improvement of the sell\-side contracting process — from deal initiation in CRM through contract creation, negotiation, redline review, and execution — enabling Sales and the procurement desk to operate more efficiently with AI assistance.
- Lead the same transformation on the buy\-side, improving how vendor and procurement contracts are created, reviewed, and approved.
- Integrate AI\-powered contracting capabilities directly into the commercial systems Sales and procurement use daily, including CRM, CPQ, and ERP, ensuring a seamless and connected experience.
- Partner with Legal to define appropriate self\-service parameters, standard playbooks, and escalation thresholds so that AI\-assisted contracting is both fast and legally sound.
AI Adoption \& In\-House Agent Development
- Maintain current, hands\-on knowledge of the best AI tools in the market for legal — including contract review, drafting assistance, clause extraction, obligation tracking, and workflow automation.
- Identify where AI can be infused into existing legal workflows and execute against those opportunities, not just plan for them.
- Evaluate and implement commercial tools such as Ironclad, SpotDraft, Lexion, and Harvey.ai with clear ROI criteria.
- Identify use cases where custom, in\-house AI agents would outperform off\-the\-shelf solutions — and partner with Engineering to build them.
- Run structured tool evaluations, pilots, and POC programs; deliver clear go/no\-go recommendations.
Cross\-Functional Execution
- Drive the contracting transformation program end\-to\-end — from vision through deployment.
- Partner with Legal, Sales Ops, Product, Engineering, and Finance to integrate CLM capabilities across the Quote\-to\-Cash stack.
- Manage vendor relationships and stakeholder engagement to drive successful platform deployments.
- Own the decisions, not just the recommendations — this role is accountable for delivery.
Workflow Optimization \& Enablement
- Redesign legal workflows to maximize speed, consistency, and scalability.
- Maintain fluency in QTC systems, contract integrations, and related data flows.
- Partner with IT, procurement, and business teams to introduce AI\-powered solutions.
- Lead change management and drive user adoption across all legal tech tools.
Metrics \& Continuous Improvement
- Define and track success metrics for efficiency, compliance, ROI, and AI impact.
- Build dashboards and analytics to measure cycle time, tool adoption, and performance.
- Establish feedback loops to drive ongoing optimization of the legal tech ecosystem.
Qualifications
Required
- Bachelor’s or Master Degree in Computer Science, Project Management, or related field.
- 10\+ years in legal innovation, product management, legal operations, or a tech\-forward in\-house/law firm environment.
- Background in precedence IT \> AI \> CLM \> Legal ( good to have)
- Deep, current knowledge of the AI tools available for contract drafting, redline review, and negotiation — and a track record of deploying them, not just evaluating them.
- Experience improving sell\-side or buy\-side commercial workflows, specifically reducing legal bottlenecks in the contract creation, negotiation, and approval process.
- Ability to design and implement AI\-assisted self\-service contracting for Sales and procurement teams, with appropriate legal guardrails.
- Experience identifying where custom AI agents or automations can fill gaps that commercial tools cannot.
- Hands\-on experience with CLM platforms (e.g., DocuSign CLM, Ironclad, Agiloft, SpotDraft) and their integration with CRM and ERP systems.
- Experience with Salesforce and Quote\-to\-Cash systems, including quoting, billing, discounting, and product catalog flows.
- Understanding of redlining, versioning, and legal approval workflows.
- Proficiency building dashboards and analytics to track cycle time, adoption, and ROI.
- Track record running tool evaluations, pilots, and POC programs with clear success criteria.
- Demonstrated ability to drive cross\-functional programs to completion — not just to alignment.
- Strong communication, stakeholder alignment, and cross\-functional leadership skills.
Preferred
- Experience in high\-growth SaaS or technology companies.
- Deep understanding of data privacy, regulatory compliance, and legal security standards.
- Familiarity with platforms such as LinkSquares, Lexion, or Harvey.ai.
Why Join Us
- Own a program with real organizational impact — this role has C\-level visibility and directly affects deal velocity and legal risk.
- Shape both strategy and execution in a role built for a builder, not a maintainer.
- Work at the frontier of AI in legal at a company already investing in the tools and infrastructure to make it real.
- Competitive compensation, equity, and flexibility.
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
- Paid parental and pregnancy leave and new parent gift boxes
- 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 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 BuiltIn, 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 is a global leader in agentic voice AI–powered business communications, delivering an integrated platform for business phone, SMS, contact center, workforce engagement management, video collaboration, and messaging. As the communications layer connecting businesses and customers, RingCentral is the front door of business communication and is in the advantageous position to apply AI at every phase of the conversation journey — before, during, and after each interaction. Our agentic AI portfolio includes autonomous voice\-first AI agents that automate calls, assist in the moment, and analyze every interaction – enabling businesses to work smarter, respond faster, and connect more meaningfully with their customers. Visit ringcentral.com to learn more.
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 $177,100 and $253,000 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 $177K-$253K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $177K to $253K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
RingCentral AI Hiring
RingCentral has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Belmont, CA, US. Compensation range: $253K - $253K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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