Full-Time

Manager – Accounts Payable & Financial Operations

Updated on 5/26/2026

CodeRabbit

CodeRabbit

51-200 employees

AI-powered automated code reviews for VCS

Compensation Overview

$150k - $180k/yr

+ Equity

San Francisco, CA, USA

In Person

Category
Finance & Banking
Accounting
Required Skills
Excel/Numbers/Sheets
Requirements
  • 5+ years of progressive accounts payable and financial-operations experience, ideally in a high-growth startup or Software as a Service environment
  • Hands-on experience with accounts payable invoice processing, GL coding, vendor management, and 1099 reporting at scale
  • Comfort owning month-end close deliverables for accounts payable and financial operations — accruals, reconciliations, and journal entry support
  • Working knowledge of payroll administration, including multi-state United States payroll, remote-state registrations, and federal/state/local payroll tax regulations
  • Proficiency in Excel / Google Sheets (VLOOKUPs / XLOOKUPs, pivot tables, reconciliations)
  • Excellent attention to detail, organizational skills, and the ability to manage competing deadlines without dropping balls
  • Strong written and verbal communication; partners credibly with People Operations, Finance, Revenue Operations, and employees at every level
  • High degree of discretion and integrity with sensitive employee and financial data
Responsibilities
  • Own day-to-day accounts payable processing in Bill.com — invoice intake, GL coding, approval routing, and payment runs on the established cadence
  • Maintain the approval matrix in Bill.com and partner with department heads on workflow exceptions
  • Own vendor onboarding end-to-end: W-9 collection, 1099 classification, banking detail verification, and master vendor record hygiene
  • Review corporate card transactions (Bill Spend / corporate card program) for supporting documentation and proper GL coding
  • Review and process employee expense reimbursements submitted through Rippling Spend in line with our Travel & Expense policy
  • Apply accurate GL coding (account, department, class) to invoices, corporate card spend, and employee expenses in line with the chart of accounts
  • Track prepaid expenses, monthly accounts payable accruals, and vendor aging; partner with the Controller on month-end AP close deliverables
  • Help drive the accounts payable system evaluation and implementation — Ramp vs. Brex vs. Rippling continuation — as we scale automation and controls
  • Support the upcoming ERP migration, ensuring AP data flows cleanly into the new general ledger
  • Partner with the team on monthly close — AP accruals, prepaid amortization, commission accruals and true-ups, payroll journal entries, and account reconciliations
  • Own the W-9 / 1099 vendor process end-to-end and serve as the accounts payable and payroll liaison for internal and external auditors
  • Help formalize and document accounts payable, travel and entertainment, procurement, and payroll standard operating procedures as we move from external-firm-led to in-house ownership
  • Support cash-flow forecasting by providing visibility into vendor payment commitments, accounts payable aging, and upcoming payroll runs
  • Partner cross-functionally with People Operations, Revenue Operations, Legal, and the broader Finance team to keep controllership operating cleanly at scale
  • Own end-to-end payroll across all CodeRabbit entities, including semi-monthly United States payroll in Rippling and monthly India payroll through our in-country provider / employer of record
  • Act as the primary point of contact for Rippling, our EOR, and any local payroll partners for United States, India, and additional geographies as we expand
  • Review and validate hours, commissions, bonuses, equity-related taxable events, and pay adjustments before each run
  • Ensure accurate calculation of wages, taxes, statutory benefits, deductions, and garnishments
  • Partner with Revenue Operations and Finance to process monthly commission payments
  • Maintain employee payroll records and system data within Rippling; audit reports before submission for accuracy and compliance
  • Coordinate with People Operations on new hires, terminations, leaves of absence, benefit changes, and compensation updates
  • Monitor and execute new-state payroll tax registrations as we hire remote talent; ensure compliance with federal, state, and local payroll tax regulations
  • Administer the Human Interest 401(k) plan — contribution funding, true-ups, testing, and audit support
  • Lead year-end payroll activities, including W-2 and 1099 preparation and reporting
  • Respond to employee payroll inquiries with accuracy, empathy, and a high bar for confidentiality
Desired Qualifications
  • Direct, recent experience in Bill.com accounts payable and Rippling payroll
  • Experience evaluating or implementing accounts payable automation platforms (Ramp, Brex, Bill.com)
  • Familiarity with QuickBooks today and a point of view on modern enterprise resource planning systems (NetSuite, Campfire, Rillet)
  • Bachelor's degree in Accounting, Finance, Business, or a related field; Fundamental Payroll Certification or Certified Payroll Professional certification a plus

CodeRabbit automates code reviews by integrating with Git hosting services (GitHub, GitLab, Azure DevOps, Bitbucket) and using OpenAI models to give contextual feedback during the first pass of a review. It analyzes code in pull requests and suggests improvements, helping teams improve code quality and speed up merges. The product is delivered as a SaaS service and a self-hosted option for enterprises, with a VS Code extension that provides AI-powered analysis directly in the IDE. It targets software development teams of all sizes and uses a subscription model with tiered plans to fit different team sizes and support needs. Overall goal: reduce manual review effort, shorten PR cycles, and decrease production bugs by providing intelligent, automated code analysis.

Company Size

51-200

Company Stage

Series B

Total Funding

$79.6M

Headquarters

Walnut Creek, California

Founded

2023

Simplify Jobs

Simplify's Take

What believers are saying

  • AI-generated code increases demand for automated review layers.
  • Enterprise buyers value governance, SAST support, and usage controls.
  • Issue Planner expands CodeRabbit into pre-PR planning workflows.

What critics are saying

  • GitHub Copilot remains a cheaper default for GitHub-centric teams.
  • Qodo Merge’s open-source core reduces CodeRabbit’s subscription advantage.
  • Free IDE and CLI access pressures paid-seat conversion and expansion revenue.

What makes CodeRabbit unique

  • CodeRabbit focuses on AI code review, not code generation.
  • It spans GitHub, GitLab, Azure DevOps, Bitbucket, VS Code, Slack, and CLI.
  • Its context engine processes millions of pull requests for thousands of teams.

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Your Connections

People at CodeRabbit who can refer or advise you

Benefits

Company Equity

Hybrid Work Options

Professional Development Budget

Growth & Insights and Company News

Headcount

6 month growth

-4%

1 year growth

2%

2 year growth

-1%
Lead Web Praxis Media Limited
May 19th, 2026
How does Matter AI compare to other AI tools like CodeRabbit?

How does Matter AI compare to other AI tools like CodeRabbit? In recent software development landscape, AI-powered code review tools are no longer optional, they are strategic assets. But how does Matter AI compare to competitors like CodeRabbit? More importantly, which one aligns better with modern engineering workflows, cost efficiency, and developer productivity? This article breaks down their differences with a human-centered lens, helping teams make informed decisions. What is Matter AI and CodeRabbit? When evaluating AI code review platforms, it's essential to understand their foundational philosophy. Matter AI positions itself as a robust, security-first code review engine that emphasizes automated summaries, vulnerability detection, and enterprise-grade integrations. It focuses on improving pull request workflows by identifying bugs, performance issues, and security risks before deployment. (SourceForge) On the other hand, CodeRabbit is designed as a context-aware AI reviewer that mimics a senior engineer. It provides line-by-line suggestions, conversational feedback, and integrates deeply into developer environments like GitHub and GitLab. (Toolbit.ai) So, here's a critical question: Should your AI tool act as a strict security auditor or a collaborative coding partner? That distinction often defines the choice. Core features comparison. A closer look at features reveals how Matter differs in execution and priorities. It excels in generating instant pull request summaries, detecting bugs, and integrating contextual data from tools like Notion, Jira, and Confluence. This makes it particularly effective for teams handling complex enterprise systems. (Toolify) Meanwhile, CodeRabbit focuses on precision and developer usability. It combines static analysis tools (linters and SAST) with AI reasoning to deliver actionable suggestions, automated test generation, and even docstrings. (Rank&Compare) An interesting AI-related thought: Is deeper analysis always better, or does simplicity improve developer adoption? In practice, both approaches solve different pain points. Performance and workflow efficiency. From a workflow perspective, Matter AI aims to reduce review cycles by automating summaries and highlighting critical issues early. Its ability to process code in isolated environments ensures privacy and compliance, especially for enterprise teams handling sensitive data. (slashdot.org) In contrast, CodeRabbit is optimized for speed and clarity. It learns from repository context and past pull requests, delivering feedback that aligns with team conventions. This reduces friction during reviews and accelerates approvals. (Gartner) Interestingly, developer discussions often suggest that CodeRabbit is easier for junior engineers to understand, while Matter AI provides deeper system-wide insights. (Reddit) Security and privacy considerations. Security is where Matter clearly differentiates itself. With SOC 2 Type II compliance and isolated processing environments, it ensures that proprietary code is never stored or reused. (slashdot.org) While CodeRabbit also offers enterprise-grade security features such as encryption and self-hosted deployments, its primary strength lies in contextual intelligence rather than strict compliance frameworks. (Rank&Compare) This raises another question: Do you prioritize airtight compliance or adaptive intelligence in your AI tools? Pricing and cost analysis. Cost plays a significant role in tool adoption. Both Matter and CodeRabbit offer competitive entry-level pricing, typically starting around $12-$15 per month, with free versions or trials available. (slashdot.org) However, the real cost difference emerges at scale. Matter AI's enterprise-grade features may justify higher costs for large organizations, while CodeRabbit's flexible deployment and usability make it appealing for startups and mid-sized teams. So, consider this: Are you optimizing for upfront cost or long-term engineering efficiency? Integrations and ecosystem. Integration capabilities often determine how seamlessly an AI tool fits into existing workflows. Matter integrates with platforms like Notion, Jira, and Confluence, enabling richer contextual analysis across project documentation. (SourceForge) Meanwhile, CodeRabbit supports GitHub, GitLab, Azure DevOps, and various IDEs, making it highly accessible for developers already embedded in these ecosystems. (Rank&Compare) The key takeaway: Matter AI leans toward organizational context, while CodeRabbit focuses on developer-centric workflows. Strengths and weaknesses. Every tool comes with trade-offs, and Matter is no exception. Its strengths lie in security, deep analysis, and enterprise integrations. However, it may feel complex for smaller teams or developers who prefer lightweight tools. Conversely, CodeRabbit shines in usability, contextual feedback, and ease of adoption. Yet, it may not provide the same depth of system-wide analysis or compliance guarantees as Matter AI. This leads to a broader AI question: Should tools aim for specialization or versatility? Which one should you choose? Choosing between Matter AI and CodeRabbit ultimately depends on your team's priorities. * If your organization values security, compliance, and deep code analysis, Matter AI is the stronger candidate. * If you prioritize ease of use, fast feedback, and developer-friendly insights, CodeRabbit may be the better fit. Both tools demonstrate how AI is transforming code review, from a manual bottleneck into an intelligent, automated process. Conclusion. In the end, the comparison between Matter AI and CodeRabbit highlights a broader shift in software engineering: AI is no longer just assisting developers, it is actively shaping how code is written, reviewed, and deployed. If your organization is looking to integrate cutting-edge AI solutions into your development workflow, the next step is strategic implementation. For expert guidance, tailored solutions, and seamless integration, clients should reach out to Lead Web Praxis Media Limited to leverage AI tools effectively and stay ahead in the competitive digital landscape.

Business Wire
Apr 2nd, 2026
CodeRabbit appoints Matthew Mulqueen as CRO to drive global expansion after $60M Series B

CodeRabbit, an AI-native code review platform, has appointed Matthew Mulqueen as Chief Revenue Officer to lead its global go-to-market strategy. Mulqueen will oversee sales, marketing and customer success as the company expands its enterprise operations. Mulqueen brings nearly two decades of experience from high-growth infrastructure and AI companies. He previously served as CRO at LILT AI and held executive roles at Kong and Cresta. He also led sales for Datadog and AppDynamics in EMEA during their growth phases through IPO. Following a $60 million Series B round, CodeRabbit is scaling regional teams across North America, EMEA and APJ to support Fortune 500 clients. The San Francisco-based company serves over 8,000 organisations and has processed millions of pull requests.

Vibe Coding
Mar 18th, 2026
CodeRabbit vs alternatives.

CodeRabbit vs alternatives. | Feature | CodeRabbit | GitHub Copilot | Qodo Merge | Sourcery | | Primary focus | Code review | Code generation (review secondary) | Code review | Code review (Python-focused) | | PR review | Full line-by-line | Basic review suggestions | Full line-by-line | Automated refactoring | | Code generation | No | Yes | No | Limited | | 1-click fixes | Yes | No | Yes | Yes | | Free tier | Yes (summaries + IDE) | Yes (limited) | Open-source core | Free for OSS | | Pro pricing | $24/dev/mo | $10/mo (individual) | $19/dev/mo | $30/dev/mo | | Git platforms | GitHub, GitLab, Azure DevOps, Bitbucket | GitHub only | GitHub, GitLab, Bitbucket | GitHub, GitLab, Bitbucket | | SOC 2 | Yes (Type II) | Via Microsoft | No | No | | Test generation | Yes | Via Copilot | Yes | No | | Custom rules | YAML config | Repository rules | Custom policies | Configuration file | | Language support | All major languages | All major languages | All major languages | Python, JavaScript, TypeScript | CodeRabbit vs GitHub Copilot: Copilot is a code generation tool with review as a side feature. CodeRabbit is a review tool and nothing else. If you already use Copilot for writing code, CodeRabbit adds dedicated review depth that Copilot's review mode doesn't match. They're complementary, not competitive. Business Process CodeRabbit vs Qodo Merge: The closest direct competitor. Qodo Merge (formerly PR-Agent) has an open-source core, which appeals to teams that want to self-host. CodeRabbit's advantages are its broader platform support (Azure DevOps), architecture diagrams, and the scale of its paid platform (10,000+ customers vs a smaller user base). Qodo is slightly cheaper at $19/dev/month. CodeRabbit vs Sourcery: Sourcery is excellent if your stack is Python-heavy. Its refactoring suggestions are more targeted for Python codebases. CodeRabbit is language-agnostic and broader in scope. Choose based on your stack. Who CodeRabbit is for. Teams doing vibe coding at scale. If your team uses AI tools to generate code - Cursor, Claude Code CLI, or any AI coding assistant - you need a review layer that can keep up. Human reviewers can't review every AI-generated PR with the same depth. CodeRabbit fills that gap. Open source maintainers. Free Pro features for public repos is a strong offer. If you maintain a project with external contributors, CodeRabbit gives you an automated first pass on every incoming PR. Open Source Teams without enough senior reviewers. Junior developers create PRs but senior reviewers are scarce. CodeRabbit acts as a tireless first reviewer, catching the obvious issues so senior devs can focus on architecture and design decisions. Security-conscious organizations. SOC 2 Type II, GDPR, HIPAA, zero retention. If your compliance team needs to sign off on every tool that touches code, CodeRabbit's security posture makes that conversation easier. Who should skip it. Solo developers. If you're the only one creating and reviewing PRs, the per-seat cost is low but the value is also lower. You might get more out of your IDE's built-in AI review features. Teams that need code generation. CodeRabbit doesn't write code. If you're looking for a tool that does both generation and review, look at GitHub Copilot or combine CodeRabbit with a dedicated coding assistant. Budget-constrained small teams. At $24/dev/month, a 10-person team pays $240/month. If that's a significant line item, the free tier's PR summaries might be enough until the budget allows an upgrade. Development Tools Verdict. CodeRabbit does one thing - automated code review - and does it well. The reviews catch real bugs, the 1-click fixes reduce friction, and the security certifications make enterprise adoption straightforward. The free tier is generous enough to evaluate before committing, and the open source offering is genuinely free with no strings attached. The main limitation is that it's review-only. In a world where most AI tools try to do everything, CodeRabbit's focused approach is both its strength and its constraint. You'll still need a separate tool for code generation, but that's fine - most teams already have one. At $24/dev/month for Pro, it's priced competitively against alternatives. If your team creates enough PRs to justify automated review - and most active teams do - CodeRabbit pays for itself by catching bugs before they reach production and freeing up senior developers to focus on higher-value work. Bottom line: If you're vibe coding and shipping fast, CodeRabbit is the safety net your review process needs. Machine Learning & Artificial Intelligence AI Tools Editor AI editorial avatar for the Vibe Coding team. Reviews tools, tests builders, ships content.

Startup Wired
Mar 17th, 2026
CodeRabbit doubles revenue in 6 months as AI code review tools surge despite needing human oversight

CodeRabbit, a San Francisco-based startup building AI tools for code review, doubled its revenue within six months of securing fresh funding. The company's platform integrates with GitHub and GitLab to analyse code in real time, identifying bugs, inefficiencies and security risks whilst suggesting improvements. Despite strong growth, founder Tudor Achim emphasised that AI will not replace software engineers. Developers still control architecture, project scope and critical decisions, whilst AI handles repetitive tasks like code review and validation. The success reflects broader industry trends, as companies adopt AI to accelerate development cycles without sacrificing quality. However, research shows AI-generated code can contain more vulnerabilities than human-written code, reinforcing the need for human oversight and validation before deployment.

NVIDIA
Mar 11th, 2026
New NVIDIA Nemotron 3 Super Delivers 5x Higher Throughput for Agentic AI

New NVIDIA Nemotron 3 Super delivers 5x higher throughput for agentic AI. A new, open, 120-billion-parameter hybrid mixture-of-experts model optimized for NVIDIA Blackwell addresses the costs of long thinking and context explosion that slow autonomous agent workflows. Launched today, NVIDIA Nemotron 3 Super is a 120-billion-parameter open model with 12 billion active parameters designed to run complex agentic AI systems at scale. Available now, the model combines advanced reasoning capabilities to efficiently complete tasks with high accuracy for autonomous agents. AI-Native Companies: Perplexity offers its users access to Nemotron 3 Super for search and as one of 20 orchestrated models in Computer. Companies offering software development agents like CodeRabbit, Factory and Greptile are integrating the model into their AI agents along with proprietary models to achieve higher accuracy at lower cost. And life sciences and frontier AI organizations like Edison Scientific and Lila Sciences will power their agents for deep literature search, data science and molecular understanding. Enterprise Software Platforms: Industry leaders such as Amdocs, Palantir, Cadence, Dassault Systèmes and Siemens are deploying and customizing the model to automate workflows in telecom, cybersecurity, semiconductor design and manufacturing. As companies move beyond chatbots and into multi-agent applications, they encounter two constraints. The first is context explosion. Multi-agent workflows generate up to 15x more tokens than standard chat because each interaction requires resending full histories, including tool outputs and intermediate reasoning. Over long tasks, this volume of context increases costs and can lead to goal drift, where agents lose alignment with the original objective. The second is the thinking tax. Complex agents must reason at every step, but using large models for every subtask makes multi-agent applications too expensive and sluggish for practical applications. Nemotron 3 Super has a 1-million-token context window, allowing agents to retain full workflow state in memory and preventing goal drift. Nemotron 3 Super has set new standards, claiming the top spot on Artificial Analysis for efficiency and openness with leading accuracy among models of the same size. The model also powers the NVIDIA AI-Q research agent to the No. 1 position on DeepResearch Bench and DeepResearch Bench II leaderboards, benchmarks that measure an AI system's ability to conduct thorough, multistep research across large document sets while maintaining reasoning coherence. Hybrid Architecture. Nemotron 3 Super uses a hybrid mixture-of-experts (MoE) architecture that combines three major innovations to deliver up to 5x higher throughput and up to 2x higher accuracy than the previous Nemotron Super model. * Hybrid Architecture: Mamba layers deliver 4x higher memory and compute efficiency, while transformer layers drive advanced reasoning. * MoE: Only 12 billion of its 120 billion parameters are active at inference. * Latent MoE: A new technique that improves accuracy by activating four expert specialists for the cost of one to generate the next token at inference. * Multi-Token Prediction: Predicts multiple future words simultaneously, resulting in 3x faster inference. On the NVIDIA Blackwell platform, the model runs in NVFP4 precision. That cuts memory requirements and pushes inference up to 4x faster than FP8 on NVIDIA Hopper, with no loss in accuracy. Open weights, data and recipes. NVIDIA is releasing Nemotron 3 Super with open weights under a permissive license. Developers can deploy and customize it on workstations, in data centers or in the cloud. The model was trained on synthetic data generated using frontier reasoning models. NVIDIA is publishing the complete methodology, including over 10 trillion tokens of pre- and post-training datasets, 15 training environments for reinforcement learning and evaluation recipes. Researchers can further use the NVIDIA NeMo platform to fine-tune the model or build their own. Use in agentic systems. Nemotron 3 Super is designed to handle complex subtasks inside a multi-agent system. A software development agent can load an entire codebase into context at once, enabling end-to-end code generation and debugging without document segmentation. In financial analysis it can load thousands of pages of reports into memory, eliminating the need to re-reason across long conversations, which improves efficiency. Nemotron 3 Super has high-accuracy tool calling that ensures autonomous agents reliably navigate massive function libraries to prevent execution errors in high-stakes environments, like autonomous security orchestration in cybersecurity. Availability. NVIDIA Nemotron 3 Super, part of the Nemotron 3 family, can be accessed at build.nvidia.com, Perplexity, OpenRouter and Hugging Face. Dell Technologies is bringing the model to the Dell Enterprise Hub on Hugging Face, optimized for on-premise deployment on the Dell AI Factory, advancing multi-agent AI workflows. HPE is also bringing NVIDIA Nemotron to its agents hub to help ensure scalable enterprise adoption of agentic AI. Enterprises and developers can deploy the model through several partners: * Cloud Service Providers: Google Cloud's Vertex AI and Oracle Cloud Infrastructure, and coming soon to Amazon Web Services through Amazon Bedrock as well as Microsoft Azure. * NVIDIA Cloud Partners: Coreweave, Crusoe, Nebius and Together AI. * Inference Service Providers: Baseten, CloudFlare, DeepInfra, Fireworks AI, Inference.net, Lightning AI, Modal and FriendliAI. * Data Platforms and Services: Distyl, Dataiku, DataRobot, Deloitte, EY and Tata Consultancy Services. The model is packaged as an NVIDIA NIM microservice, allowing deployment from on-premises systems to the cloud. Stay up to date on agentic AI, NVIDIA Nemotron and more by subscribing to NVIDIA AI news, joining the community, and following NVIDIA AI on LinkedIn, Instagram, X and Facebook.