Machine Learning Engineer (PhD or MS Required) 756

Reposted 24 Days Ago
Palo Alto, CA, USA
Hybrid
189K-212K Annually
Mid level
Information Technology • Security
The Role
The Machine Learning Engineer will develop and test GenAI architectures, fine-tune LLMs, apply ML algorithms, and contribute to architectural design in a collaborative environment.
Summary Generated by Built In

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 seeking a Machine Learning Engineer (PhD or Master’s degree required) to join our GenAI team at a pivotal moment in the market. As enterprises race to adopt Generative AI, securing AI workflows, data, and privacy has become mission‑critical, and Protegrity is at the forefront of making GenAI safe for real world, enterprise use.

This role is designed for a PhD or Master’s graduate with 2+ years of GenAI experience (or equivalent advanced research or technical projects), who wants to work hands‑on building secure AI workflows and agentic systems in a highly collaborative, hybrid/in‑office 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.

Requirements:

  • PhD or Master's Degree required

  • 2+ years of hands-on GenAI experience or equivalent projects.

  • 2+ years of hands-on Python coding experience.

  • Experience with PyTorch, TensorFlow.

  • Solid understanding of ML algorithms and metrics.

  • Exposure to data security, data privacy, cybersecurity practices an asset.

  • Strong collaboration and learning mindset

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 Privacy Policy 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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The Company
HQ: Salt Lake City, UT
372 Employees
Year Founded: 1996

What We Do

Protegrity protects the world's most sensitive data wherever it resides. Our industry-leading solutions allow businesses to finally tap into the value of their data and accelerate digital transformation timelines – without jeopardizing individuals’ fundamental right to privacy.

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