Hiring
in Palo Alto
Fast growing, High Revenue, Gen AI.
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Join us
Featured roles
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AI Researcher: Post-Training Palo Alto, CA
Responsibilities
- Data generation
- Reward model training
- Research and algorithm implementation
Tech stack
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Full Stack: Monetization Palo Alto, CA
Responsibilities
- Subscription, checkout and payments flows
- Pricing and paywall experiments
- Revenue analytics and reporting
Tech stack
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Talent Acquisition Palo Alto, CA
Responsibilities
- Sourcing top engineers and researchers
- Coordinating the interview process from first contact to offer
- Building CHAI's hiring brand
Skills
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Backend: AI Infra and GPU Orchestration Palo Alto, CA
Responsibilities
- Model serving and inference infrastructure
- GPU scheduling and orchestration across providers
- Scaling, reliability and cost efficiency
Tech stack
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Front-End: App & Web Palo Alto, CA
Responsibilities
- Building features for the CHAI app and website
- UI performance and polish
- Shipping and iterating on product experiments
Tech stack
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Interview process
Please send your resume to will@chai-research.com.
Overview
We run a fast process — an offer can be given within 7 days of first contact. We have no leetcode, behavioural interviews, or design interviews. Assessments are done via take-home and onsite coding challenges, both of which are project-based to reflect an engineer's or researcher's daily work at CHAI.
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Step 1
Initial chat
We do not use recruiters for screening. Instead, an engineer or our founder will have a 20-minute initial video call with you.
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Step 2
Personality assessment
We'll send you a personality assessment to understand you better. This is the one we use.
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Step 3
Take home challenge
We'll send you a short take home challenge that typically takes 60-90 minutes to complete.
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Step 4
Onsite interview
You'll be invited onsite to meet the team and complete the second half of the take home.
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Step 5
Offer
CHAI offers are all-cash. We will offer at least 1.5x over existing TC for all candidates.
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Visas
We support H-1B transfers, and we sponsor O-1 and EB-1 visas.
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Evidence
Outside sources and our own research, grouped by who made them.
Editorial coverage
Made and published by an independent outlet.
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Reuters: CHAI’s $120M annual revenue (opens in a new tab)
Reuters reports $120M in annual revenue and interviews William Beauchamp about CHAI’s focus on revenue that covers compute costs.
Read the source context and CHAI’s annualized revenue definition.
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Dealroom: CHAI’s $100M milestone (opens in a new tab)
Dealroom covers CHAI’s growth past $100M in annualized revenue with a team of around 15, drawing on company-reported figures.
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Latent Space: The AI Engineer Podcast (opens in a new tab)
Alessio Fanelli and swyx interview our founder, William Beauchamp, at our Palo Alto office.
5 engineering topics, with timestamps
- 25:59Scaling the backend past 500k DAU, and cutting a 30-day A/B loop to about 3 hours
- 46:49Chaiverse: serving submitted models to live users and ranking them by Elo
- 57:14Evaluation: human feedback versus static benchmarks
- 1:07:20Inference cost; no streaming, so replies can be rejection-sampled
- 1:09:37Reward models trained on which messages users reply to
Commercial relationship
Published by a company with a financial stake in CHAI.
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AMD Ventures portfolio (opens in a new tab)
AMD Ventures, AMD's venture capital arm, is an investor in CHAI; the listing reflects that investment, not an independent assessment of our work.
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CoreWeave on LinkedIn (opens in a new tab)
CoreWeave Ventures is an investor in CHAI; the post reflects that investment, not an independent assessment of our work.
CHAI-authored research
Written by our team, about experiments on our platform.
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Rewarding Chatbots for Real-World Engagement with Millions of Users (opens in a new tab)
Shows that filtering replies with a reward model trained on user-interaction signals raised mean conversation length by up to 70% and retention by over 30% for GPT-J 6B in live A/B tests.
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Blending Is All You Need (opens in a new tab)
Shows that randomly blending replies from three 6B/13B models matched or beat ChatGPT (175B+) on engagement and retention in 30-day A/B tests.
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CHAI AI: Post Training to Achieve SOTA MoE (opens in a new tab)
Reports post-training results on open models including DeepSeek-V4 and Kimi-K2: GSPO beat GRPO in A/B tests, and the latest in-house model lifted Day-30 retention by 4%.