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Hebbia AI

Platform Engineer, Document Intelligence

New York, United States +1

Location
New York, United States · San Francisco, California, United States
Work
On-site
Employment
Full-time
Level
Senior
Function
Engineering
Pay
$160K – $350K a year (read from the ad)
Posted
16 months ago

Pay and the market here

The top of this role’s range, $350K a year, is 52% above the median of $230K for senior engineering jobs in New York (middle half $200K – $250K, 1,001 jobs that state pay).

3,709 engineering jobs open in New York: 12% remote, 695 posted in the last 30 days.

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About the role

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 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 Document Intelligence team at Hebbia builds cutting-edge AI solutions that transform how users discover and interact with billions of private and public documents. Our products, including the Hebbia’s Browse application, enable intelligent document exploration, powerful search capabilities, and deep insights extraction. We focus on developing advanced data ingestion and search technologies that deliver intuitive, explainable, and highly responsive experiences. Working closely with customers, our team continuously iterates to address real-world challenges and drive impactful, data-driven decisions. Our goal is to empower users by seamlessly turning vast and complex document repositories into actionable intelligence.

The Role

Platform engineering at Hebbia is about excellent, scalable enablement. You are responsible for the core distributed systems that power billions of tokens across millions of dollars of AUM. You will be responsible for deploying efficient systems and building software tightly coupled with state of the art infrastructure/system design. Hebbia’s edge is built on operating on the edge of the tokenomics curve and you will serve as a key contributor in this area. We value engineers who think on their feet, innovate and can solve for exponential scale.

Responsibilities

  1. Own critical system components: Take complex requirements and turn them into robust, scaled solutions that solve real customer needs.
  2. Unlock O(1) universal indexing: Build and iterate on our high-scale document build system that enables constant time latency for indexing any content in the world, regardless of data volume.
  3. Drive performance optimization: Architect and implement performance-tuning solutions to ensure our systems operate efficiently at scale, minimizing latency and maximizing throughput across millions of documents.
  4. Mentor and guide: Provide technical leadership, mentorship, and guidance to junior engineers, fostering a culture of learning and growth.

Who You Are

  • Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field. A strong academic background with coursework in data structures, algorithms, and software development is preferred.
  • 5+ years software development experience at a venture-backed startup or top technology firm, with a focus on distributed systems and platform engineering.
  • Proficiency in building backend and distributed systems using technologies such as Python, Java, or Go.
  • Deep understanding of scalable system design, performance optimization, and resilience engineering.
  • Extensive experience with cloud platforms (e.g., AWS)
  • Working experience with one or more of the following: Kafka, ElasticSearch, PostgreSQL, and/or Redis
  • Knowledge of workflow orchestration and execution platforms like Airflow, Temporal or Prefect.
  • Proven experience enabling observability patterns.
  • Ability to analyze complex problems, propose innovative solutions, and effectively communicate technical concepts to both technical and non-technical stakeholders.
  • Proven experience in leading software development projects and collaborating with cross-functional teams. Strong interpersonal and communication skills to foster a collaborative and inclusive work environment.
  • Enthusiasm for continuous learning and professional growth. A passion for exploring new technologies, frameworks, and software development methodologies.
  • Autonomous and excited about taking ownership over major initiatives
  • Bonuses:
  • Experience building distributed systems leveraging technologies such as etcd or Apache Zookeeper.
  • Frequent user of AI products, especially during the development lifecycle (i.e. Cursor, Claude Code, etc).

Compensation

The salary range for this role is $160,000 to $350,000. 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

PTO: Unlimited

Insurance: Medical + Dental + Vision + 401K

Eats: Catered lunch daily + doordash dinner credit if you ever need to stay late

Parental leave policy: 3 months non-birthing parent, 4 months for birthing parent

Fertility benefits: $15k lifetime benefit

New hire equity grant: competitive equity package with unmatched upside potential

#LI-Onsite

Source

Posted by Hebbia AI on their own hiring page, jobs.ashbyhq.com. OptiTalent shows it as published, read 07 Nov 2026 11:28 UTC; the original is the reference.

View the original ad ↗

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