Analytical and Problem solving, Effective communication skills, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), Natural Language Toolkit - NLTK, Python Programming, spaCy, Statistical modeling
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
Who You Are
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.
Hebbia's Matrix solution serves as a collaborative partner in knowledge-intensive industries like finance, law, government, and pharmaceuticals. Matrix excels in handling intricate tasks by deconstructing them into manageable LLM actions. Users can team up with Matrix to efficiently extract, organize, and analyze vast document collections, enabling seamless workflow automations leveraging LLM capabilities.