At a glanceSummarised by Seekless from the posting.
Must have8
Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field, and 4+ years of experience
Building agentic AI applications in Python using agent orchestration frameworks such as LangGraph, LangChain, Agent SDKs, or equivalent, including multi-step planning, tool use, and structured output generation
Demonstrated ability to adopt and migrate between emerging agentic frameworks, protocols, and tooling, including Model Context Protocol (MCP), as the ecosystem evolves
Designing and implementing evaluation frameworks for large language model and agentic systems, including construction of ground-truth datasets, output-level and trajectory-level metrics, and evaluation-driven system improvement
Applying large language models to production problems, including prompt design, retrieval-augmented generation, context management, and containment of non-determinism within reviewable boundaries
Reasoning over structured and semi-structured data across multiple systems, including schema interpretation, entity relationship modeling, and querying using SQL and related data retrieval tools
Foundational machine learning knowledge sufficient to reason about model behavior, failure modes, and evaluation validity
Deploying and operating services in cloud environments such as AWS, including containerized deployment, tracing, and observability tooling; and using generative AI tools to rapidly prototype and deliver production-ready solutions
Skills
Python
LangGraph
LangChain
Agent SDKs
Model Context Protocol (MCP)
SQL
AWS
generative AI
About the role
Foresight builds AI-native capabilities that change how quickly Intuit can turn data into products customers can use. As an Senior AI Scientist, you will design and build agentic systems that solve open-ended problems and produce accurate, deterministic outputs that reach customers in production.
The problems rarely arrive well-specified. You will be asked to make an agent reason its way through an unfamiliar problem space, work out what it needs and how the pieces relate, and hand a human expert something reviewable and correct. Containing non-determinism so that a person can trust what comes out is central to the work.
We hold accuracy above latency and cost, in that order, and we expect that tradeoff to be argued explicitly rather than assumed. The agentic ecosystem is moving quickly, and the frameworks we build on today will not be the frameworks we build on in a year. We are looking for someone who treats that as an advantage rather than a risk.
Responsibilities
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Design, build, and improve agentic AI systems in Python that plan across open-ended problem spaces, resolve what they need, and derive the logic required to produce a correct result.
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Produce reviewable, deterministic artifacts from agent runs — logic that a human expert can inspect, correct, and approve, and that executes at runtime without a large language model in the path.
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Design human-in-the-loop review experiences that give domain experts genuine visibility and control before anything reaches production.
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Define and own evaluation methodology for agentic systems: construct ground-truth evaluation sets from production data, design metrics for both final-output correctness and agent trajectory quality, and drive system improvement from measured gaps rather than anecdote.
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Reason across entities and information originating in different systems, including tracing them back through code and schema to their systems of record, and partnering with owning teams to establish access.
Take capabilities from experimental environments through to production, including responsible AI review, service deployment, tracing and observability, and operational readiness.
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Evaluate and adopt emerging agentic frameworks, protocols, and tooling, and migrate existing systems between them as the ecosystem evolves.
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Prototype rapidly using generative AI, and independently identify and unblock cross-team dependencies rather than waiting on them.
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Partner with product managers, domain experts, and engineering teams outside of direct reporting lines to align capabilities with business outcomes.
Qualifications
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Master’s degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field, and 4+ years of experience in the job offered or in an AI Scientist, Machine Learning, or Data Science related occupation.
•
Building agentic AI applications in Python using agent orchestration frameworks such as LangGraph, LangChain, Agent SDKs, or equivalent, including multi-step planning, tool use, and structured output generation.
•
Demonstrated ability to adopt and migrate between emerging agentic frameworks, protocols, and tooling, including Model Context Protocol (MCP), as the ecosystem evolves.
•
Designing and implementing evaluation frameworks for large language model and agentic systems, including construction of ground-truth datasets, output-level and trajectory-level metrics, and evaluation-driven system improvement.
•
Applying large language models to production problems, including prompt design, retrieval-augmented generation, context management, and containment of non-determinism within reviewable boundaries.
•
Reasoning over structured and semi-structured data across multiple systems, including schema interpretation, entity relationship modeling, and querying using SQL and related data retrieval tools.
•
Foundational machine learning knowledge sufficient to reason about model behavior, failure modes, and evaluation validity.
•
Deploying and operating services in cloud environments such as AWS, including containerized deployment, tracing, and observability tooling; and using generative AI tools to rapidly prototype and deliver production-ready solutions.
Benefits
Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.