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Machine Learning Engineer 756
Machine Learning Engineer 756Protegrity • Palo Alto, California, United States
Machine Learning Engineer 756

Machine Learning Engineer 756

Protegrity • Palo Alto, California, United States
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At Protegrity, we lead innovation by using AI and quantum-resistant cryptography to transform data protection across cloud-native, hybrid, on-premises, and open source environments. We leverage advanced cryptographic methods such as tokenization, format-preserving encryption, and quantum-resilient techniques to protect sensitive data. As a global leader in data security, our mission is to ensure that data isn’t just valuable but also usable, trusted, and safe.

Protegrity offers the opportunity to work at the intersection of innovation and collaboration, with the ability to make a meaningful impact on the industry while working alongside some of the brightest minds. Together, we are redefining how the world safeguards data, enabling organizations to thrive in a GenAI era where data is the ultimate currency. If you're ready to shape the future of data security, Protegrity is the place for you.

Protegrity is looking for a Machine Learning Engineer (MS Required)

Location : Menlo Park, CA (In-office, Mon–Thu)

The global data privacy software market is projected to grow from $2.36 billion in 2022 to $25.85 billion by 2029. Join us on this journey and make an impact with one of the top 25 global software providers. We look forward to making our world become a better place with you on our team.

About the Role :

This role is designed for MS graduates in Computer Science with 2+ years of GenAI experience or equivalent technical projects.

You’ll work on securing AI workflows and building agentic tools in a collaborative, fast-paced environment.

Responsibilities :

Develop and test GenAI architectures using agentic coding IDEs.

Conduct experiments and summarize findings.

Present research and experimental results to the team.

Fine-tune LLMs and embedding models.

Apply ML algorithms to large datasets.

Process structured and unstructured data.

Participate in architectural design and roadmap discussions.

Qualifications :

MS in Computer Science.

2+ years GenAI experience or equivalent projects.

3–5 years Python experience.

Experience with PyTorch, TensorFlow.

Solid understanding of ML algorithms and metrics.

Exposure to data security practices.

Strong collaboration and learning mindset.

Why Choose Protegrity :

Become a member of a leading Data Protection, Privacy and Security company during one of the best market opportunities to come along in a generation.

Competitive Compensation / Total Reward Packages that include :

Health Benefits (Health / Dental / Vision)

Paid Time Off (PTO)

401K

Annual Bonus Incentives

Short and Long Term Disability

Work on global projects with diverse, energetic, team members who respect each other and celebrate differences

Talent First Workforce

Should you accept this position, you will be required to consent to and successfully complete a background investigation. This may include, subject to local laws, verification of extended education and additional criminal and civil checks.

We offer a competitive salary and comprehensive benefits with generous vacation and holiday time off. All employees are also provided access to ongoing learning & development.

Ensuring a diverse and inclusive workplace is our priority. We are committed to an environment of acceptance where you are free to bring your full self to work. All qualified applicants and current employees will not be discriminated against on the basis of race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability or veteran status.

Please reference Section 12 : Supplemental Notice for Job Applicants in our  to inform you of the categories of personal information that we collect from individuals who inquire about and / or apply to work for Protegrity USA, Inc., or its parent company, subsidiaries or affiliates, and the purposes for which we use such personal information.

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