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Principal AI Engineer
Principal AI EngineerRhythms • San Francisco, California, United States
Principal AI Engineer

Principal AI Engineer

Rhythms • San Francisco, California, United States
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  • [job_card.full_time]
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About Rhythms

Rhythms is building the AI operating system that enables enterprises to become AI-native—fundamentally rethinking how organizations operate, execute, and scale. Founded by entrepreneurs who built and sold Chronus (acquired by Private Equity) and Ally.io (acquired by Microsoft), we've raised one of the largest seed rounds of $26M from top investors including GreenOaks, Madrona, and Accel, alongside CXOs from global technology leaders, earning GeekWire's 2024 Deal of the Year award and named a 2025 finalist for AI Innovation of the Year.

Rhythms transform how teams operate by streamlining team operating processes—from meetings and KPI tracking to OKRs and strategic planning. Built on a multi-agent architecture that leverages foundational AI models, our platform seamlessly connects with communication tools like Slack and Teams, project management systems like Jira and Linear, and other work tools teams use daily. We turn meetings into structured workflows, eliminate manual updates with real-time insights, and build organizational memory that grows smarter through patterns, interactions, and knowledge captured across systems.

Our enterprise customers chose us because we're solving the hardest problem : making agentic AI systems reliable enough to run critical business operations.

As Principal AI Engineer, you'll work closely with CEO Vetri Vellore (ex-Microsoft VP, founded Chronus and Ally.io ) alongside leaders and engineers from IIT, Stanford, and other top-tier institutions who ship production AI daily. Based in San Francisco Bay Area, CA, you'll be at the forefront of building the AI operating system that defines how enterprises run on artificial intelligence.

What We're Looking For

AI-First Builder – Deep expertise in production-ready AI systems and agentic workflows, not just demos

Ship Fast, Learn Faster – Entrepreneurial ownership mentality with bias toward shipping and iterating in production

Technical Leadership – Proven ability to architect complex AI systems, build evals-first, and make data-backed decisions

System Design Excellence – Strong architectural skills to design scalable, maintainable systems that balance complexity and pragmatism

Clear Communicator – Translate technical tradeoffs and risks effectively to both engineering teams and business stakeholders

Passion for the Cutting Edge – Deep curiosity and drive to explore, apply, and productionize the newest AI models and technologies

Required Skills And Experience

AI Engineering Excellence

7+ years building production software with at least 2+ years deeply focused on LLMs, agents, or AI-native applications

Expert proficiency in Python and experience with React / Next.js for AI-powered UIs

Experience shipping agentic systems to production.

Experience with RAG implementations : Hybrid search, Re-ranking, context optimization, and retrieval quality measurement.

Technical Leadership

Designed and architected distributed systems at production scale.

Proven ability to influence technical direction across multiple teams while mentoring engineers

Track record of driving ambiguous, high-impact initiatives from concept to measurable business outcomes

Effective collaboration across distributed teams spanning US and India time zones

Preferred Qualifications

Bachelor's or Master's degree in Computer Science or related field, or equivalent experience with demonstrable technical excellence

Contributed to open source AI projects or published research

Background in high-scale distributed systems

Based in or willing to relocate to San Francisco Bay Area

Why Join Rhythms - Your Opportunity To

Shape the Future of Enterprise AI

Build the core AI systems powering autonomous execution for thousands of organizations

Your technical leadership will define how enterprises actually run on AI—chat-native experiences, multi-agent orchestration, enterprise-grade reliability

Work with cutting-edge tech stack

Work With World-Class Team

Collaborate with engineers from IIT, Stanford, and top-tier institutions who've scaled systems to millions

Learn from CEO Vetri who is a Microsoft Veteran and a serial entrepreneur with successful exits

Join at 15 people where your code and architectural decisions directly shape the product—no layers between you and impact

Own Meaningful Equity

Significant equity stake at early stage (15 people = meaningful ownership)

$26M runway, paying enterprise customers, sustainable path—build the right product, not chase next fundraise

Work with founders who've built and sold companies—they know what matters

Real Impact, Immediately

Day one : You'll be shipping code to production—we deploy daily

First week : Your code will be running in production for enterprise customers

First month : You'll own major features that thousands of users depend on

Six months : Your architecture decisions will define how enterprises operate with AI

Join customer calls, hear feedback directly, and see your code transform how organizations work in real-time

Embrace Entrepreneurial Environment

Take ownership of technical decisions with real autonomy—strong opinions backed by evals get shipped

AI-native culture : use Cursor and Claude Code for development, Claude Code reviews PRs, ship AI features weekly

Flat structure, direct feedback, open technical debates—better ideas win

If you want FAANG stability, this isn't it. If you want to define a category, it is.

How to Apply?

Apply with your resume.

GitHub profile showing AI engineering work (especially : agentic systems, eval frameworks, production LLM applications)

If best work is closed-source, describe the architecture, challenges, and impact in your application

Include projects you have worked on, with technical details. For each project, include a one-liner description of the problem you were trying to solve, the technology you chose to use, the challenges you faced, and the scale (RPM) of the project. What you learned and what you'd do differently now.

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