Applied AI Engineer, Staff at Hebbia

About Hebbia

The user interface for AGI – Hebbia is AI that works the way you work.  

Designed to be generally capable– it can tackle even the most complex tasks, citing answers over any amount of sources.   By showing its work, Hebbia empowers users to collaborate with AI on each step and validate responses instead of blindly trusting them.  Our mission is to put capable AI in the hands of 1 billion people by 2030.

Job Description

As a Staff level Applied AI Engineer, you will be the bridge between research, industry, and application shaping the future of our core natural language processing systems. You will be responsible for enabling agentic capabilities across the Hebbia product suite. You will own experiments and POCs focused on combining the latest research findings with specific high value problems that our customers encounter each and every day. You will leverage our deep relationships with foundation model providers -  partnering to beta test models, experiment with new features, and develop guidance on relative model strengths

This role requires prior expertise in NLP, machine learning systems, and LLM evaluation; experience building with foundation models and experience working with Attention based NLP models is a plus. This role is best suited for an individual who can excel at both running experiments with novel LLM techniques as well as building production grade, LLM-enabled software systems - embedding directly in the software development lifecycle. 

Our team works in person 5 days a week at our SoHo office in NYC.

Responsibilities

• Focused on LLMs, you will play a crucial role in analyzing and interpreting complex data types to derive and implement cutting edge insight generation systems.
• Iterate and explore new LLM and NLP techniques maintaining our foothold as an industry leader
• You will utilize your expertise in statistics, programming, and machine learning to develop and deploy data-driven models and algorithms
• Your work will contribute to solving business problems, improving processes, and enhancing the overall performance of the company
• Collaborate with cross-functional teams to improve NLP/LLM capabilities in app
• Stay up-to-date with the latest advancements and research in the space 
• Collaborate with software engineers to integrate agentic capabilities into existing systems or develop new applications
• Ensure that systems are efficient, maintainable and well monitored.
• Iterate on validation and testing frameworks.

Who You Are

• Bachelor's degree in Computer Science, Engineering, or related field
• Master’s degree in Computer Science, Mathematics, Machine Learning or a related field is a plus.
• 7+ years software development experience at a venture-backed startup or top technology firm, with a focus on applied machine learning systems
• Strong programming skills in Python
• Experience with NLP and text processing libraries such as NLTK, SpaCy, or Apache Tika
• Experience with Search and Indexing technologies
• Proficient in machine learning techniques and algorithms
• Experience working with foundational models and corresponding APIs
• Knowledge of statistical analysis and data scraping techniques
• Prior experience in developing NLP models and systems
• Experience with prompting and building LLM applications and agents is a plus.
• Excellent problem-solving and analytical skills
• Strong communication and teamwork abilities
• Strong capability to translate research into production software systems

Compensation

In consideration of market analysis and relevant factors, the salary range for this position is set between $215,000 and $275,000. However, adjustments outside of this range may be considered for candidates whose qualifications significantly differ from those outlined in the job description. Additionally, this role is eligible to participate in our equity plan and benefits program. Benefits include, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, daily catered lunch, and unlimited PTO.

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