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Privacy Preserving Machine Learning Jobs in Texas

Capital One

Lead Machine Learning Engineer

Plano, TX · On-site

$98K - $129K/yr

Lead Machine Learning Engineer As a Capital One Lead Machine Learning Engineer (MLE), you'll be ... Ensure all AI/ML applications strictly adhere to robust data privacy standards, regulatory postures ...

webAI Inc

Senior Forward Deployed AI Engineer

Austin, TX · On-site

$103K - $142K/yr

This role sits at the intersection of machine learning, systems engineering, and deployment ... Knowledge of privacy-preserving AI and secure compute environments. * Master's degree in a relevant ...

Quora

$139K - $168K/yr

At Poe, we use Machine Learning in various parts of the product - bot routing, agent flow, code ... Job Applicant Privacy Notice: LI-SS2 LI-REMOTE

Quora

$139K - $168K/yr

At Poe, we use Machine Learning in various parts of the product - bot routing, agent flow, code ... Job Applicant Privacy Notice: LI-SS2 LI-REMOTE

Quora

$139K - $168K/yr

At Poe, we use Machine Learning in various parts of the product - bot routing, agent flow, code ... Job Applicant Privacy Notice: LI-SS2 LI-REMOTE

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All JobsPrivacy Preserving Machine Learning JobsPrivacy Preserving Machine Learning Jobs in Texas

Privacy Preserving Machine Learning information

What are some common challenges faced by professionals working in privacy preserving machine learning roles?

Professionals in Privacy Preserving Machine Learning often encounter challenges such as balancing model accuracy with strict privacy requirements, selecting appropriate privacy-preserving techniques (like differential privacy or federated learning), and ensuring compliance with evolving data protection regulations. Collaborative projects may also involve coordinating with legal, data security, and software engineering teams to implement robust solutions. Additionally, staying updated with the latest research and adapting to new threats or vulnerabilities is a continuous part of the role.

What is the difference between Privacy Preserving Machine Learning vs Data Scientist?

AspectPrivacy Preserving Machine LearningData Scientist
Required CredentialsTypically requires knowledge of machine learning, data privacy, and security certificationsRequires degrees in data science, statistics, or related fields; certifications like Certified Data Scientist are common
Work EnvironmentWorks in research, development, and implementation of privacy-focused ML models, often in tech or finance sectorsAnalyzes data, builds models, and provides insights across various industries including marketing, finance, and healthcare
Employer & Industry UsageUsed by organizations prioritizing data privacy, such as healthcare, finance, and tech companiesEmployed across diverse sectors for data analysis, predictive modeling, and decision support

Privacy Preserving Machine Learning focuses on developing models that protect data privacy during training and inference, while Data Scientists analyze and interpret data to generate insights. Both roles require strong analytical skills, but Privacy Preserving Machine Learning emphasizes security and privacy techniques, whereas Data Scientists focus on data analysis and modeling.

What is privacy preserving machine learning?

Privacy preserving machine learning refers to techniques and methods that allow data analysis and model training while protecting sensitive information. This field focuses on ensuring that personal or confidential data is not exposed or compromised during the development and deployment of machine learning models. Approaches such as federated learning, differential privacy, and homomorphic encryption are commonly used. These methods enable organizations to leverage data for insights and predictions without violating privacy regulations or risking data breaches. Privacy preserving machine learning is especially important in industries like healthcare, finance, and any sector handling personal data.

What are the key skills and qualifications needed to thrive as a privacy preserving machine learning engineer?

To thrive as a Privacy Preserving Machine Learning Engineer, you need a strong background in machine learning, data privacy techniques (such as differential privacy or federated learning), and a relevant degree in computer science or a related field. Familiarity with frameworks like TensorFlow Privacy, PySyft, and privacy-enhancing technologies, along with certifications in data security or privacy, are often required. Strong problem-solving abilities, meticulous attention to detail, and the ability to communicate complex technical concepts clearly set top professionals apart. These skills ensure the development of robust machine learning models that protect sensitive data while delivering valuable insights, maintaining compliance and trust.
What are popular job titles related to Privacy Preserving Machine Learning jobs in Texas? For Privacy Preserving Machine Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Privacy Preserving Machine Learning jobs in Texas look for? The top searched job categories for Privacy Preserving Machine Learning jobs in Texas are:
What cities in Texas are hiring for Privacy Preserving Machine Learning jobs? Cities in Texas with the most Privacy Preserving Machine Learning job openings:
Privacy Preserving Machine Learning jobs near you
Infographic showing various Privacy Preserving Machine Learning job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Lead Machine Learning Engineer

Capital One

Plano, TX • On-site

$98K - $129K/yr

Full-time

Re-posted 27 days ago

Capital One rating

7.7

Company rating: 7.7 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

93rd of 171 rated banks

Job description

Lead Machine Learning Engineer
As a Capital One Lead Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing Generative AI and advanced agentic systems at scale. You'll lead the detailed technical design, development, and implementation of core agentic architectures and multi-agent workflows using emerging technologies. You'll focus on system-level architectural design, develop and review complex models and application code, and ensure the high availability, performance, and security of our generative AI applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in generative and agentic machine learning engineering.
What you'll do in the role:
  • Architect Agentic Platforms: Design, develop, and scale core agentic engines and multi-agent workflow solutions, enabling seamless composition of conversational and business automation workflows.
  • Drive AI Evaluation & Trust: Build and integrate scalable evaluation (Evals) and observability frameworks into solutions to ensure model predictability, performance monitoring, and mitigation of model risk.
  • Deliver High-Impact Use Cases: Partner with cross-functional product and business teams to deploy production AI solutions, including next-generation consumer AI experiences, intelligent recommendation engines, and advanced conversational assistants.
  • Enforce Enterprise Guardrails: Ensure all AI/ML applications strictly adhere to robust data privacy standards, regulatory postures, and framework auditability/explainability.
  • Translate Practical Research: Stay abreast of practical advancements in LLM optimization, retrieval-augmented generation (RAG), and multi-agent design patterns, judiciously applying these novel techniques to production systems.
  • Technical Leadership & Code Excellence: Provide technical direction, architectural oversight, and rigorous code reviews for engineering teams, fostering a culture of modern engineering excellence.

Basic Qualifications:
  • Bachelor's Degree
  • At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
  • At least 4 years of experience programming with Python, Scala, or Java

Preferred Qualifications:
  • Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
  • 3+ years of experience with GenAI frameworks (e.g., LangChain, LangGraph, LlamaIndex) and Vector Databases
  • 3 years of experience building, scaling, and optimizing Large Language Model (LLM) or GenAI orchestration systems in production
  • 2+ years of experience building automated evaluations (Evals) and observability pipelines for LLMs
  • 3+ years of on-the-job experience with an industry-recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
  • Experience deploying AI solutions within a strictly regulated environment, incorporating data privacy and model risk governance
  • Demonstrated ability to lead technical architecture design and provide deep technical guidance to engineering teams
  • Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
  • ML industry impact through conference presentations, papers, blog posts, open-source contributions, or patents

At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).
The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
Plano, TX: $179,400 - $204,700 for Lead Machine Learning Engineer
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.
This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.
Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
This role is expected to accept applications for a minimum of 5 business days.
No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com
Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.
Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

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