Staff Machine Learning Engineer

Intuit

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Quick summary

Work type
On-site
Location
Mountain View, CA
Salary
$230,000–$250,000 / yr
Posted
64 days ago
Freshness
Confirmed live today

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $230k
This role $240k
$173k $294k
below market most similar roles pay here above market

This role pays less than 51% of similar roles. Most pay $206,175–$254,750 — the blue band above. At the midpoint, this role pays about $240k versus about $230k for comparable roles.

Based on 240 similar postings.

Employer

About Intuit

Intuit is a financial software company known for products like TurboTax, QuickBooks, Mint, and Credit Karma, helping consumers and small businesses manage their finances and taxes. Industry: Financial Software & Technology

Intuit currently has 255 open roles on FindRole.

Listed pay typically runs $202,500–$274,000 across 236 roles with salary data.

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At a glance

TL;DR · Staff Machine Learning Engineer

Staff Machine Learning Engineer The Staff Machine Learning Engineer will join the team to build AI/ML systems at the core of products across personal finance, accounting, and tax. This role involves designing and owning shared AI capabilities, including model training pipelines, evaluation frameworks, and ML tooling. Key responsibilities include building long-term memory systems for context retention, developing agent builder frameworks, and moving agentic AI concepts from proof-of-concept to production. The engineer will apply ML fundamentals and LLM techniques like prompting, fine-tuning, and RAG to solve customer problems. Required technical skills include Python, PyTorch, TensorFlow, Pandas, NumPy, and LangChain. Candidates must also possess experience with AWS, SageMaker, JavaScript, Java, Kubernetes, and relational or NoSQL data stores to architect full-stack, AI-native applications and scalable services.

What does a Machine Learning Engineer earn in California?

Median $235750 from 200 postings across 34 companies.

See salary data

What you'll do

  • Design and own shared AI/ML components, including model training pipelines, evaluation frameworks, and fine-tuning tools.
  • Apply LLM techniques like prompting, fine-tuning, and RAG to solve specific customer problems and evaluate production performance.
  • Build long-term memory and context-retention systems to enable personalized and coherent AI experiences.
  • Prototype and implement agent builder frameworks and embedded AI experiences from early exploration to production.
  • Drive rapid prototyping and experimentation to move from proof-of-concept to high-accuracy, performant systems.
  • Architect and build full-stack, AI-native applications from backend services to production LLM integrations.
  • Set best practices for ML tooling, developer workflows, and application architecture while mentoring other engineers.

What we're looking for

  • BS, MS, or PhD in Computer Science or equivalent practical experience.
  • 8+ years of experience building production AI/ML systems.
  • Strong CS fundamentals in data structures, algorithms, and system design.
  • Solid ML fundamentals including classification, regression, clustering, and neural networks.
  • Proficiency in Python and standard ML frameworks like PyTorch, TensorFlow, Pandas, and NumPy.
  • Practical experience with LLMs, including prompt engineering, fine-tuning, and LangChain.
  • Experience building agentic systems, agent frameworks, or long-term memory/context architectures for AI applications.
  • Proficiency in one or more full-stack languages (JavaScript, Java) and experience with microservices, relational/NoSQL data stores, and Kubernetes.

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