Role Overview
Intuit is hiring a mid-level Machine Learning Engineer II Recommendation Systems- Credit Karma. This is a full-time role in Oakland, California. Part of Intuit's Backend hiring. The posted range is $141k to $190k. Full responsibilities, required qualifications, and the apply link are listed in the description below.
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Job Description
Intuit Credit Karma’s Online RecSys team is looking for a Software Engineer II/Software Engineer III. Our team is focused on delivering personalized financial recommendations that empower millions of our members to make informed financial decisions.Our team develops state-of-the-art recommendation platform, including (but not limited to) deep learning based retrieval and ranking systems for personalized recommendations. You'll work on building and optimizing the backend services that powers these recommendations, collaborating closely with other software engineers, data scientists, and data engineers. This is a role where you can make an impact on how millions of people manage their financial live.
Responsibilities
- Develop and Optimize our recommendation platform.You will contribute to a wide variety of software engineering projects that power our recommendation platform and help deliver value to our members
- You will design and implement scalable solutions that handle large volumes of real-time events
- Contribute to system performance tuning and scalability to ensure low latency of recommendations
- You’ll work closely with product, data science and other engineering teams and be in charge of the development of your product features--you'll scope and independently solve moderately complex problems
- Identifying ways to improve our technology and process
- Participating in code and technical design reviews
Qualifications
- Proficient in Scala or Java with solid foundation in object-oriented programming, design patterns, algorithms, and system architecture
- Strong problem-solving skills with the ability to work through complex ambiguous challenges and collaborate effectively within a team
- Strong understanding of deployment strategies and production infrastructure needs
- You bring a strong sense of empathy, while still being able to challenge the status quo
- MS/PhD in Computer Science, ML , Information Sciences or related fields is a plus
- Experience with using cloud services and designing distributed systems is a plus
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. The expected base pay range for this position is: $140,500-$190,000
Frequently Asked Questions
How do I apply for the Machine Learning Engineer II Recommendation Systems- Credit Karma position at Intuit?
Use the Apply button above to submit your application directly to Intuit. Most applications take less than 5 minutes if your resume and contact details are ready, and you'll be routed to the employer's official application system to finish.
Where is the Machine Learning Engineer II Recommendation Systems- Credit Karma position at Intuit located?
This position is based in Oakland, California. Intuit has not indicated remote or hybrid options for this role, so candidates should plan for on-site work.
How much does the Machine Learning Engineer II Recommendation Systems- Credit Karma role at Intuit pay?
Intuit has posted a compensation range of $141k to $190k for this position. Final offers typically vary based on candidate experience, location, and internal salary bands.
When was the Machine Learning Engineer II Recommendation Systems- Credit Karma role at Intuit posted?
This role was posted on April 6, 2026 (63 days ago). It's still listed as actively hiring; we re-confirm openings against the source system multiple times per day and remove closed roles.
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