채용
복지 및 혜택
•Unlimited Pto
•Healthcare
•401(k)
•Equity
•Meals
•Parental Leave
필수 스킬
Full-Cycle Recruiting
Technical Recruiting
Sourcing
Stakeholder Management
Candidate Assessment
About Hebbia
The AI platform for investors and bankers that generates alpha and drives upside.
Founded in 2020 by George Sivulka and backed by Peter Thiel and Andreessen Horowitz, Hebbia powers investment decisions for Black Rock, KKR, Carlyle, Centerview, and 40% of the world’s largest asset managers. Our flagship product, Matrix, delivers industry-leading accuracy, speed, and transparency in AI-driven analysis. It is trusted to help manage over $30 trillion in assets globally.
We deliver the intelligence that gives finance professionals a definitive edge. Our AI uncovers signals no human could see, surfaces hidden opportunities, and accelerates decisions with unmatched speed and conviction. We do not just streamline workflows. We transform how capital is deployed, how risk is managed, and how value is created across markets.
Hebbia is not a tool. Hebbia is the competitive advantage that drives performance, alpha, and market leadership.
The Team
At Hebbia, the Talent team is not a support function. It’s a core driver of the company’s ability to build, ship, and scale category-defining AI. We partner directly with founders, executives, and functional leaders to design teams that can solve the hardest knowledge-work problems in the world.
The Talent team runs high-signal, high-conviction recruiting processes, combining deep role fluency, rigorous assessment, and decisive execution to consistently raise the bar. More than filling seats, we act as strategic advisors and operators.
The Role
As a Recruiter at Hebbia, you’ll play a pivotal role in scaling the team behind our Matrix platform by identifying, engaging, and closing exceptional talent across Engineering, Product, and Design (EPD) teams. You’ll be a strategic partner to hiring managers and leadership by shaping hiring plans, translating business priorities into crisp role definitions, and guiding candidates through a high-bar, high-touch process. Beyond filling roles, you’ll help build the foundation of Hebbia’s culture and velocity by ensuring we attract people who can reimagine how work gets done and can move fast in ambiguity.
Responsibilities
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Own EPD talent pipeline: Lead full-cycle recruiting for Hebbia’s commercial organization, including Engineering, Product, Design and Security.
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Hunt for elite EPD talent: Go far beyond inbound. Proactively source and engage top-tier technical experts through targeted outreach, creative search strategies, and deep network leverage.
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Be the face of Hebbia in the market: Serve as the first touchpoint for technical candidates. Clearly articulate Hebbia’s ambition, product differentiation, and growth trajectory ensuring every candidate is left with a compelling understanding of the opportunity.
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Deliver a world-class candidate experience: Ensure every candidate interaction is thoughtful, transparent, and high-integrity.
Who You Are
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2+ years of EPD recruiting experience, ideally at a fast-growing startup
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Proven track record rapidly scaling technical teams
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Creativity in problem-solving and innovating across all stages of the hiring cycle
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Experience aligning internal stakeholders on job descriptions, compensation bands and interview processes
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Autonomous and excited about taking ownership over major hiring initiatives
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Extreme passion for learning and growth
총 조회수
0
총 지원 클릭 수
0
모의 지원자 수
0
스크랩
0
비슷한 채용공고
Hebbia 소개

Hebbia
Series BHebbia is an American technology company that develops artificial intelligence and automation tools for financial and legal research. The company was founded in 2020 by George Sivulka, a former Stanford University PhD student, with its headquarters in New York City.
51-200
직원 수
New York
본사 위치
$1.3B
기업 가치
리뷰
4.0
10개 리뷰
워라밸
3.2
보상
3.8
문화
4.1
커리어
4.2
경영진
3.5
75%
친구에게 추천
장점
Flexible work hours and remote options
Great team culture and supportive environment
Good benefits and perks
단점
Heavy workload and overwhelming demands
Long hours during peak projects
Compensation could be better
연봉 정보
12개 데이터
Junior/L3
Junior/L3 · Analyst
1개 리포트
$156,000
총 연봉
기본급
$120,000
주식
-
보너스
-
$156,000
$156,000
면접 경험
62개 면접
난이도
3.4
/ 5
소요 기간
14-28주
합격률
37%
경험
긍정 66%
보통 20%
부정 14%
면접 과정
1
Phone Screen
2
Technical Interview
3
System Design
4
Behavioral
5
Team Fit
자주 나오는 질문
Tell me about a challenging project
System design question
Coding problem
Why this company
뉴스 & 버즈
Show HN: An unstructured data workspace for data transformations with LLM
hi HN!<p>a couple of months ago I had to analyze a few thousand audio recordings to help identify issues with customer support. i was able to get some raw high-level initial results with python scripts invoking LLM APIs, but they were too general and unhelpful. writing basic prompts is easy, but tuning them and making them specific enough to ensure no faint signal is missed is hard. you need to iterate through the data with an initial prompt, segment the data into different buckets, chain anothe
HN
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3w ago
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4
Official Reddit Home of Hebbia | A New Era of Institutional Intelligence
Founded in 2020 by George Sivulka, Hebbia was purpose-built to meet the demands of finance. Today, Hebbia is the leading AI platform for institutional finance—backed by Andreessen Horowitz, Peter Thiel, and Index Ventures. We are trusted by investment banks and over 40% of the largest asset managers by AUM trust Hebbia to work the way they do: fast, accurate, and collaborative across the full deal cycle. We have joined Reddit to connect with the financial professionals who use Hebbia to move f
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3w ago
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1
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1
Seyfarth Leads Next Phase of Deal Execution and Diligence Through AI Partnership with Hebbia - Business Wire
Business Wire
News
·
5w ago
Neuromorphic sphere topology Hebbian learning as a path to grounded intelligence
I've been working on a hypothesis and want to get feedback from people who know more than I do. The hypothesis Intelligence might be a phase transition at scale, not an algorithmic problem. Fly: 100k neurons — no generalization Mouse: 70M — basic associative learning Human: 86B — abstract reasoning This doesn't look like a smooth curve. It looks like thresholds. If that's true, then no amount of architectural cleverness crosses it — only scale + grounding does. The grounding probl
HN
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5w ago
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