Software engineer 1

Overview

Company Overview

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

About The Team

Foresight builds advanced AI technology that turns Intuit's proprietary data and financial domain expertise into experiences that deliver real customer value. We build the foundational models and agentic systems behind Intuit's financial intelligence systems, and behind Financial Intuit Language Models, our family of fine-tuned models for financial intelligence. Our work reaches millions of small and medium-sized businesses across QuickBooks and other products, and is grounded in the richness of data few companies have.

Job Overview

Come join Foresight as a Software Engineer 1. This is an early-career position on a team that takes AI capabilities from experiment to production, and you will be writing the code that makes that happen — the services that serve our models and agents, the pipelines that feed them data, the evaluation harnesses that tell us whether they actually work, and the interfaces that let people use them.

You will work alongside AI scientists, AI Builders, ML engineers, product managers, and designers, and you will get unusually direct exposure to how large language models and agentic systems are built, measured, and shipped. We expect you to arrive curious and leave the year meaningfully stronger as an engineer. Senior engineers on the team will pair with you, review your code, and hand you increasing ownership as you earn it.

Responsibilities

• Implement features, services, and bug fixes in Python and Java within the team's existing architecture, with guidance from senior engineers.
• Contribute to backend services that support serving LLMs and AI agents, including the APIs and integrations that connect them to Intuit products.
• Help build and maintain data pipelines that prepare, clean, and transform data used for model training and evaluation.
• Support the team's evaluation work — extend benchmark and eval harnesses, run experiments, and help turn results into clear reports and dashboards.
• Build internal tools, prototypes, and proofs of concept that let scientists and product partners try out new capabilities quickly.
• Write well-documented, testable code, add automation tests, and participate in peer code reviews.
• Debug and resolve defects across testing, pre-production, and production with support from more experienced engineers.
• Learn and apply engineering best practices: version control workflows, CI/CD, secure software development, and test-driven development.
• Build a working understanding of the financial and accounting domain our models operate in — bookkeeping, transaction categorization, and autonomous accounting workflows.
  
  

• Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
• Proficiency in Python, plus working knowledge of at least one additional language such as Java. Coursework, internship, or personal project experience is fine at this level.
• Solid computer science fundamentals: data structures, algorithms, and complexity.
• Working knowledge of version control (Git/GitHub), testing, and basic software design patterns.
• Genuine interest in AI: academic knowledge, internship experience, or personal projects involving LLMs, agents, or machine learning, and a desire to build AI products at scale.
• Strong problem-solving mindset, a collaborative attitude, and a demonstrated ability to learn fast in an environment that changes quickly.
• Self-motivated and proactive, with a "can-do" approach and comfort asking for help early.
• Good written and verbal communication in English and Hebrew, and flexibility to overlap with US colleagues on a semi-regular basis.
  
  

• Exposure to LLM tooling and frameworks such as HuggingFace, LangChain, agent SDKs, or vLLM.
• Familiarity with cloud and container technologies — AWS, Docker, Kubernetes — or with an ML lifecycle tool such as MLflow.
• Frontend experience (React, TypeScript, HTML/CSS) for building internal tools and demos.
• Comfort working in a Linux environment and scripting end-to-end workflows.
• Interest in the financial, accounting, or fintech domain.