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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
The Integrations team is the connective layer between Hebbia’s AI platform and every data source our customers rely on. We build and maintain the pipelines that ingest content from enterprise systems—Snowflake, S3, SharePoint, Dropbox, and beyond—so Hebbia can reason over it.
This is infrastructure work with direct customer impact. When an integration is fast, reliable, and seamless, our customers trust Hebbia with their most critical workflows. When it breaks, they feel it immediately. The team owns that end-to-end: building new connectors, hardening existing ones, and keeping the data flowing.
The Role
This is not a generic backend engineering role with integrations sprinkled in. You will spend the majority of your time building, debugging, and operating data integrations across dozens of third-party platforms. The work is hands-on, customer-facing in its impact, and requires someone who finds satisfaction in making complex systems work reliably at scale.
You will own integrations from ideation through production—designing the connector, writing the code, deploying it, monitoring it, and fixing it when something breaks at 2am. You will develop deep expertise in how enterprise data systems actually behave in the wild, which is rarely how the documentation says they should.
Strong Integrations Engineers at Hebbia become the go-to experts on the data layer that powers everything else. The role offers a high-growth trajectory for engineers who want to take on increasingly complex technical problems over time.
Responsibilities
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Build and maintain integrations with enterprise data platforms (Snowflake, S3, SharePoint, Dropbox, Google Drive, Box, and others) and customer’s self hosted storage solutions that enable customers to securely connect their data to Hebbia
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Own integration reliability end-to-end: monitoring, alerting, on-call rotation, and incident response for the data ingestion layer
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Debug and resolve integration failures across a wide surface area of third-party APIs, auth flows, and data formats—often under time pressure from active customer deployments
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Design and implement robust ingestion pipelines that handle edge cases gracefully: rate limits, partial failures, schema changes, large file volumes, and inconsistent API behavior
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Improve internal tooling and observability to reduce mean time to detection and resolution for integration issues
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Collaborate with product and customer success teams to scope new integration requests and prioritize based on customer impact
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Write clean, well-tested code that other engineers can maintain and extend
Who You Are
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2–5 years of software engineering experience, ideally at a startup or high-growth technology company
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Strong proficiency in Python, with experience building backend services and APIs
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Direct experience building or maintaining integrations with third-party APIs—you understand OAuth flows, webhook patterns, rate limiting, pagination, and the ways APIs break in practice
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Comfortable with cloud infrastructure (AWS preferred) and tools like Kafka, PostgreSQL, Redis, or Elastic Search
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Operational mindset: you take ownership of systems in production, not just in a PR. You are comfortable with on-call and treat reliability as a first-class concern
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Strong debugging instincts—you can trace a data issue from a customer report through logs, API responses, and queue state to root cause
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Clear communicator who can explain technical issues to non-technical stakeholders when a customer’s integration is down
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Autonomous and self-directed. This role has high ownership and low hand-holding
Bonuses:
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Experience with enterprise data platforms (Snowflake, Share Point, Salesforce) from the integration side
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Familiarity with document processing pipelines or content extraction systems
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Experience building agentic systems or LLM-enabled products
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Frequent user of AI tools in your development workflow (Cursor, Claude Code, etc.)
Compensation
The salary range for this role is $160,000 to $265,000. This range may be inclusive of several career levels at Hebbia and will be narrowed during the interview process based on the candidate’s experience and qualifications. Adjustments outside of this range may be considered for candidates whose qualifications significantly differ from those outlined in the job description.
Life @ Hebbia
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PTO: Unlimited
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Insurance: Medical + Dental + Vision + 401K
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Eats: Catered lunch daily + Door Dash dinner credit if you ever need to stay late
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Parental leave: 3 months non-birthing parent, 4 months for birthing parent
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Fertility benefits: $15k lifetime benefit
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New hire equity grant: Competitive equity package with unmatched upside potential
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关于Hebbia

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
员工数
New York
总部位 置
$1.3B
企业估值
评价
4.0
10条评价
工作生活平衡
3.2
薪酬
3.8
企业文化
4.1
职业发展
4.2
管理层
3.5
75%
推荐给朋友
优点
Flexible work hours and remote options
Great team culture and supportive environment
Good benefits and perks
缺点
Heavy workload and overwhelming demands
Long hours during peak projects
Compensation could be better
薪资范围
12个数据点
Junior/L3
Junior/L3 · Analyst
1份报告
$156,000
年薪总额
基本工资
$120,000
股票
-
奖金
-
$156,000
$156,000
面试经验
62次面试
难度
3.4
/ 5
时长
14-28周
录用率
37%
体验
正面 66%
中性 20%
负面 14%
面试流程
1
Phone Screen
2
Technical Interview
3
System Design
4
Behavioral
5
Team Fit
常见问题
Tell me about a challenging project
System design question
Coding problem
Why this company
新闻动态
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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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
Seyfarth Leads Next Phase of Deal Execution and Diligence Through AI Partnership with Hebbia - Business Wire
Business Wire
News
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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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