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Privacy Preserving Machine Learning Jobs in Dallas, TX

Mars Technominds Inc

Data Science Architect

Mckinney, TX · On-site

$59 - $76/hr

The ideal candidate will have strong expertise in Data Science, Machine Learning, Python, SQL ... Ensure data quality, governance, security, privacy, and compliance requirements are incorporated ...

Tiger Analytics Inc.

ML Ops Architect

Dallas, TX · On-site +1

Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations. * Collaborate with platform engineers to effectively manage cloud ...

Tiger Analytics Inc.

ML Ops Architect

Dallas, TX · Remote

Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations. * Collaborate with platform engineers to effectively manage cloud ...

Tiger Analytics Inc.

ML Ops Architect

Dallas, TX

Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations. * Collaborate with platform engineers to effectively manage cloud ...

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All JobsPrivacy Preserving Machine Learning JobsPrivacy Preserving Machine Learning Jobs in Dallas, TX

Privacy Preserving Machine Learning information

See Dallas, TX salary details

$98.4K

$114.3K

$128.1K

How much do privacy preserving machine learning jobs pay per year?

As of Sep 2, 2026, the average yearly pay for privacy preserving machine learning in Dallas, TX is $114,261.00, according to ZipRecruiter salary data. Most workers in this role earn between $99,900.00 and $127,600.00 per year, depending on experience, location, and employer.

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 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 are popular job titles related to Privacy Preserving Machine Learning jobs in Dallas, TX?

For Privacy Preserving Machine Learning jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Privacy Preserving Machine Learning jobs in Dallas, TX look for?

The top searched job categories for Privacy Preserving Machine Learning jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Privacy Preserving Machine Learning jobs?

Cities near Dallas, TX with the most Privacy Preserving Machine Learning job openings:

Privacy Preserving Machine Learning jobs near you

AI Engineering Manager_Machine Learning

VeeRteq Solutions Inc.

Irving, TX • Remote

Contractor

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Role: Sr AI Engineering Manager_Machine Learning

Experience: - Minimum 10+ Years

Location: - USA Remote

Hiring Type: - C2C

 
 

    We are looking for a AI Engineer to design, build, and deploy high-quality AI-powered features with end-to-end ownership from prototyping to production, ensuring reliable, scalable, and impactful AI solutions.

    Responsibilities: -

    End-to-End AI Feature Ownership

    • Design and implement AI-powered features (LLM workflows, copilots, and agent-based systems with tool use and multi-step reasoning)
    • Own the Full Lifecycle: prototyping → evaluation → production deployment → iteration
    • Ensure solutions are reliable, performant, and aligned with product needs

    AI System Implementation

    • Build and optimize prompt pipelines for specific use cases
    • Build retrieval systems (embeddings, chunking, ranking)
    • Implement RAG-based workflows where needed
    • Iterate on outputs to improve quality, accuracy, and consistency
    • Design scalable and cost-efficient AI architectures for production workloads
    • Select and evaluate models (hosted vs open-source) based on use case constraints

    Agent-Based Systems (AgentCore)

    • Design and build agentic workflows capable of multi-step reasoning and decision-making
    • Integrate agents with tools, APIs, and internal systems to perform real-world actions
    • Implement planning, execution, and reflection loops for complex tasks
    • Manage context, memory, and state across multi-step interactions
    • Balance deterministic workflows vs. agent autonomy for reliability and control

    Experimentation & Evaluation

    • Run structured experiments to compare approaches (prompting, retrieval, models)
    • Define and track key metrics for AI performance (quality, latency, cost)
    • Debug and improve non-deterministic system behavior
    • Build and maintain evaluation datasets and benchmarks
    • Implement automated evaluation pipelines for continuous improvement

    Collaboration & Contribution

    • Drive technical direction and influence AI adoption across teams
    • Partner with product managers and designers to scope AI features
    • Contribute to shared patterns and reusable components
    • Participate in code reviews and design discussions
    • Support and mentor mid-level engineers where needed

    AI Reliability, Safety & Governance

    • Design guardrails to ensure safe and reliable AI behavior
    • Mitigate hallucinations, prompt injection, and model misuse
    • Ensure compliance with data privacy and enterprise requirements
    • Implement monitoring and observability for AI systems in production
    • Implement guardrails for agent actions (tool access control, execution boundaries)
    • Prevent failure cascades in multi-step agent

     

    Educational Qualifications: -

    • Engineering Degree – BE/ME/BTech/MTech/BSc/MSc.
    • Technical certification in multiple technologies is desirable.

     

    Skills: -

    Mandatory skills

    Core AI Skills

    • Strong understanding of LLM capabilities and limitations
    • Experience with prompt engineering and structured output design
    • Hands-on experience with embeddings and vector search
    • Familiarity with RAG architectures and when to apply them
    • Experience designing agent-based architectures (AgentCore concepts)
    • Understanding of tool use, planning strategies, and memory mechanisms in LLM systems

    Engineering Skills

    • 5+ years of related work experience
    • Solid backend/system design fundamentals
    • Experience building and deploying production-grade systems
    • Ability to debug complex issues, including probabilistic outputs
    • Comfort working with APIs, pipelines, and data flows

    Product Thinking

    • Ability to translate user needs into effective AI solutions
    • Strong intuition for balancing quality, latency, and cost
    • Focus on delivering measurable product impact

    Collaboration

    • Communicates clearly across engineering and product teams
    • Contributes to team knowledge and shared practices.

     

    Good to have skills

    • Evaluate agent performance across multi-step tasks (task success rate, error propagation)
    • Debug and optimize agent decision-making and tool selection behavior
    VeeRteq Solutions is an Equal Opportunity Employer