Rubrik: Senior Machine Learning Engineer — Palo Alto, CA
Rubrik lists a Senior Machine Learning Engineer opening in Palo Alto, CA. The job description covers Python, Azure, machine learning, AI, audit, training, stakeholder management.
Recruitment overview
| Organisation | Rubrik |
|---|---|
| Post / role | Senior Machine Learning Engineer |
| Department | Engineering |
| Employment type | Not specified |
| Notification / job ID | 7942501 |
| Job location | Palo Alto, CA |
| Work arrangement | Not specified |
| Educational qualification | Degree / qualification: Education: A Bachelor's degree (or higher) in Computer Science, Machine Learning, Computer Engineering, Statistics, or a closely related technical field is required |
| Experience required | Experience references: 2+ years |
| Salary / stipend | See employer notice for pay details |
| Last date to apply | See official notice |
About Rubrik
Rubrik provides data-security and resilience technology for organisations facing cyberattacks, malicious activity and operational disruption. Its recruitment material discusses protecting enterprise information, including data held in business software and cloud environments. The described portfolio includes security and protection for SaaS applications, with attention to access, backup and recovery. Its business connects the technical handling of information with an organisation's ability to keep critical data protected and to recover when normal operations are disrupted.
The position and its setting
Rubrik lists Senior Machine Learning Engineer in Palo Alto, CA, under reference 7942501. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.
- Department: Engineering
- Employer listing date: 16 June 2026
Subjects named in the job description
The posting mentions the following work areas. These references are not a separate list of compulsory certifications.
- Python
- Azure
- machine learning
- AI
- audit
- training
- stakeholder management
- mining projects
Essential requirements and candidate background
- Education: A Bachelor's degree (or higher) in Computer Science, Machine Learning, Computer Engineering, Statistics, or a closely related technical field is required
- Designing production SLM training and serving systems requires a deep theoretical understanding of modern deep learning, optimization, and systems performance
Preferred experience and additional strengths
- Deep background in AI safety and red-teaming, including hands-on experience with adversarial ML, prompt injection defense strategies, and automated evaluation suites for enterprise-grade LLM safety
- Expertise in model evaluation methodology, specifically building "LLM-as-judge" pipelines, calibration monitoring, and adversarial benchmarks that surface the subtle failure modes static metrics often overlook
- Experience: context-fusion and retrieval systems that synthesize disparate signals - such as data sensitivity, user identity, and behavioral history - into high-fidelity model decisions
- Production experience with low-latency inference for streaming or safety-critical request paths where model throughput and P99 SLOs are paramount
- Mastery of label-efficient training and data mining, utilizing weak supervision, active learning, and embedding-based retrieval to surface the production examples that drive the most significant quality improvements
- Hands-on knowledge distillation experience, successfully transferring capabilities from frontier teacher models to specialized, small-scale student models for production serving
- Familiarity: the agentic ecosystem, including tool-use frameworks, model gateway architectures (MCP, LiteLLM, or equivalent), and autonomous agent patterns
- Active open-source contributions to mainstream ML training, serving, or evaluation libraries
- The minimum and maximum base salaries for this role are posted below; additionally, the role is eligible for bonus potential, equity and benefits
- The range displayed reflects the minimum and maximum target for new hire salaries for the role based on U.S. location
- Within the range, the salary offered will be determined by work location and additional factors, including job-related skills, experience, and relevant education or training
- US Pay Range
Location, schedule and contract conditions
For this overseas or regional listing, confirm work authorisation and any location restrictions with the employer. The location does not establish visa sponsorship or permission to work remotely from India.
- Advertised location: Palo Alto, CA
Pay and application dates
A verified salary figure is not established in this article. An online estimate for another Senior Machine Learning Engineer vacancy is not an offer for reference 7942501; use the employer’s own pay disclosure and recruitment communication.
A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.
How to apply to Rubrik
Applications follow the official employer page linked below.
Open the official Rubrik posting below and match the title and reference 7942501.
Compare your qualifications and experience with the essential conditions for the Palo Alto, CA position, then read the preferred criteria separately.
Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.
Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.
Practical preparation: Python
Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.
Relevant skills
- MCP
- AI/ML
- LLMs
- Python
- Azure
- machine learning
- AI
- audit
- training
- stakeholder management
- mining projects
Important links
| Official notification / job description | Open official page |
|---|
Read the complete official notice before applying. Availability and requirements can change.
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