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Open nowPosted 402 days ago

Senior Applied AI Engineer

CodeRabbit45 open roles

Pay
$200,000 – $300,000 a year
Where
San Francisco
Work mode
Hybrid
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Your applicationOpen nowSenior Applied AI EngineerCodeRabbit · San Francisco

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. CodeRabbit postings stay open a median of 46 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 402 days ago

CodeRabbit median: 46 days open

The posting

ABOUT CODERABBIT

CodeRabbit is the leading AI code review platform, trusted by more than 17,000 customers and 150,000 open-source projects, conducting over 2 million code reviews each week. We build the symbiotic partnership between developers and AI that makes shipping fast software safe again, reviewing every pull request, IDE change, and CLI commit so teams can move quickly without breaking things.

We are a fast-moving, well-funded company, fresh off a $143M Series C at a $1.5B valuation — building Agentic Change Management, the control layer for software changes created by humans and agents. As AI writes more of the world's code, the bottleneck moves from implementation to judgment and helping human judgment scale is exactly the problem we exist to solve.

ROLE OVERVIEW

As an Applied Gen AI Engineer at CodeRabbit, you'll play a central role in designing, building, and deploying advanced generative AI systems that power our code review and developer productivity tools. You’ll be responsible for bringing the latest advancements in generative AI to life — integrating techniques like RAG, RLHF, and multi-step agentic reasoning into high-impact product workflows.

You’ll collaborate with engineers, product managers, and technical leads to iterate on intelligent systems that deliver real-world value, improving how developers write, review, and ship code.

RESPONSIBILITIES

- Design and optimize LLM-based systems for high-quality, context-rich code reviews

- Build and refine agentic workflows that reason across multiple steps and contexts

- Develop and maintain knowledge base and retrieval pipelines (e.g., chunking, embeddings, semantic search)

- Deploy generative AI models and pipelines into production and monitor performance

- Collaborate across teams to ensure that AI outputs align with user needs and product goals

- Analyze human-in-the-loop feedback and usage data to iteratively improve system performance

- Apply RLHF, ranking, and reward modeling techniques to improve response quality over time

- Stay current with the latest generative AI developments and apply them to new use cases

QUALIFICATIONS

- Education: Degree in Computer Science, Engineering, Artificial Intelligence, or related field, or equivalent practical experience

- Experience: 5+ years applying ML or LLM-based systems in real-world production environments, with at least 2 years of industry experience focused on generative AI

- Technical Skills: Strong programming skills in TypeScript and Python

- AI Frameworks: Experience with tooling such as LangChain, LlamaIndex, OpenAI APIs, or vector databases like Pinecone or Lancedb

- Prompt Engineering: Strong skills in prompt engineering

- Data Fluency: Ability to extract insight from telemetry, logs, user signals, and structured feedback

- Practical Mindset: Comfortable applying research-inspired methods to solve concrete product challenges

- Cross-Functional Collaboration: Experience working across product, engineering, and design to deliver production-grade systems

BONUS POINTS

- Experience optimizing RAG systems and tuning retrieval performance using custom embeddings or search strategies

- Hands-on experience with RLHF pipelines, reward modeling, or behavioral policy tuning in LLMs

- Experience integrating LLM systems into developer tooling or collaborative workflows

- Track record of contributions to open-source projects or publications in applied AI/ML

WHY JOIN OUR ENGINEERING CULTURE?

- CodeRabbit is building the next generation of AI-native developer tooling — starting with code review. We combine large language models with deep software engineering context to help teams ship faster, catch more bugs, and make better architectural decisions at scale.

- We are a high-ownership engineering culture. That means no passive execution, no waiting for perfect tickets, and no narrowly defined task boundaries. Engineers here find problems before they're assigned, use AI as a core part of how they build, ship with judgment, and own outcomes from proposal to production.

- Our operating philosophy: bias toward action, ship the smallest necessary coherent slice, validate proportional to risk, watch what happens, and make the system better. AI drafts; humans decide. Speed matters, but so does understanding what you ship.

- This opportunity will be energizing for people who want real ownership, pace, and high standards. It's uncomfortable for people who prefer slow consensus or heavily managed workflows.

- If you want to build tools that are changing how software gets written, and be held to the standard that the best engineers thrive under; we'd love to talk.

OUR VALUES

- 🤝 Collaborative Humans — Prioritizing collective intelligence

- 🚀 Fearless Innovators — Turning obstacles into growth opportunities

- 💪 Persistent, Passionate Developers — Thriving on complex, long-term challenges

- 🎯 Impact-Driven Creators — Crafting intuitive tools for developers

- 🧠 Rapid Learners and Un-learners — Adapting quickly in our fast-paced technological world

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