Sr Machine Learning Engineer - Identity Security & Continuous Authentication

Rocket Lab; Sunnyvale (CA); On-site; USD 198,000 - 220,000; Full time

Job summary

A leading tech company seeks a Senior Machine Learning Engineer to enhance security systems using AI/ML. This role focuses on building intelligent authentication systems, leading initiatives to detect malicious behaviors, and integrating machine learning into secure application frameworks. Candidates should possess strong ML expertise, security knowledge, and proven leadership capabilities.

Qualifications

• 8+ years of experience in ML or applied data science.
• Proficiency in Python, Go, or Java.
• Deep experience with productionizing ML models.

Responsibilities

• Design and build a risk signal aggregator.
• Develop ML models for behavioral biometrics.
• Collaborate with engineers to integrate ML into authentication.

Skills

Machine Learning

Security Fundamentals

Python

Go

Java

TensorFlow

PyTorch

Scikit-learn

Leadership

Job description

About the Role

We are looking for a Senior Machine Learning Engineer with experience at the intersection of machine learning and security to join Uber’s Security Engineering team. This is a strategic role focused on technical leadership and execution to build the next generation of context-aware, intelligent authentication systems.

You’ll lead initiatives that use AI/ML to detect and react to malicious actors, enable continuous authentication, and power step-up authentication based on user behavior and real-time risk signals without compromising user experience. Your work will be foundational to how Uber’s global workforce and partners authenticate securely and seamlessly across internal and third-party applications.

What the Candidate Will Do

1. Design and build a risk signal aggregator that consumes data from internal and third-party tools (e.g., CrowdStrike, network logs, device posture signals)
2. Develop ML models to analyze behavioral biometrics (typing speed, typical login times, device usage, etc.)
3. Enable step-up authentication (or revocation of sessions) based on dynamic risk scores
4. Collaborate with identity, MFA, and SSO engineers to integrate ML models into the authentication flow
5. Build feedback loops and detection pipelines to learn and adapt to new attack vectors over time
6. Partner with our detection and incident response teams to proactively block malicious behavior
7. Lead engineering reviews, mentor other engineers, and evangelize AI-driven security internally
8. Collaborate with the Machine Learning Infrastructure (Michelangelo) team to help them adapt to our growing needs and portfolio

Minimum Qualifications

1. 8+ years of experience in ML or applied data science, with a focus on real-time systems
2. Strong understanding of security fundamentals and attack patterns (e.g., phishing, account takeover, session hijacking)
3. Proficiency in Python, Go, or Java, and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn
4. Deep experience with productionizing ML models in high-scale, low-latency environments
5. Strong communication and leadership skills, with experience setting a vision or roadmap

Preferred Qualifications

1. Experience building risk scoring models or anomaly detection pipelines
2. Experience with context-aware or continuous authentication systems
3. Background in identity and access management (IAM) or fraud prevention
4. Familiarity with behavioral biometrics, federated identity, or user/session analytics
5. Experience working with tools like CrowdStrike, Duo, MDMs, or network monitoring tools
6. Contributions to security ML research, or patents/publications in this area
7. Experience building or integrating with identity and authentication systems (e.g., Okta, SSO, MFA, ID verification)

Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.

Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.

For San Francisco, CA-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.

For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits.

Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.

Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.