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Privacy Preserving Machine Learning Jobs in Oregon

AI Engineer, Sr

Newberg, OR · On-site

$109K - $150K/yr

... maintain machine learning models and AI services used in production environments • Design and ... security, privacy, and responsible AI standards • Collaborate with software engineers, data ...

Familiarity with HIPAA and healthcare data privacy standards. * Experience supporting machine learning pipelines, feature engineering workflows, or feature stores. * Experience with streaming ...

Deep knowledge of AI/ML concepts and patterns, including machine learning, generative AI, large ... Strong understanding of AI security, privacy, governance, and compliance requirements, including ...

Deep knowledge of AI/ML concepts and patterns, including machine learning, generative AI, large ... Strong understanding of AI security, privacy, governance, and compliance requirements, including ...

Design and build statistical models and machine learning solutions that surface trends, risk ... Familiarity with privacy-preserving statistical methods for aggregate analytics * Familiarity with ...

Showing results 21-40

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 job categories do people searching Privacy Preserving Machine Learning jobs in Oregon look for? The top searched job categories for Privacy Preserving Machine Learning jobs in Oregon are:
What cities in Oregon are hiring for Privacy Preserving Machine Learning jobs? Cities in Oregon with the most Privacy Preserving Machine Learning job openings:

AI Engineer, Sr

A-dec Inc.

Newberg, OR • On-site

$109K - $150K/yr

Full-time

Re-posted 3 days ago


A-dec rating

8.8

Company rating: 8.8 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

2nd of 51 rated furniture manufacturers


Job description

Job Summary:
A-dec Inc. is committed to delivering high-quality products and services for the dental industry while providing a rewarding employment experience. The AI Engineer, Sr will play a crucial role in developing and implementing applied artificial intelligence solutions that enhance automation, decision making, and predictive insights across various business functions.
Responsibilities:
• Design, develop, validate and deploy AI driven solutions that support automation, predictive analytics, forecasting, and decision support across the business
• Build and maintain machine learning models and AI services used in production environments
• Design and orchestrate multi agent AI systems, including LLM based agents, agent routing and collaboration, and MCP enabled data and tool APIs for secure, scalable enterprise workflows
• Build and optimize LLM capabilities using retrieval augmented generation, model fine tuning, and interface integration to deliver reliable, high quality AI outputs in production environments
• Integrate AI capabilities with existing enterprise systems such as ERP, CRM, data platforms, and workflow tools
• Implement data pipelines and feature engineering processes to support reliable model training and inference
• Evaluate and integrate third party AI platforms, APIs, and tools where appropriate
• Establish best practices for model deployment, monitoring, performance tuning, and lifecycle management
• Support enterprise data governance by partnering with data owners to define data contracts and ensure data quality and consistency across pipelines
• Ensure AI solutions meet security, privacy, and responsible AI standards
• Collaborate with software engineers, data engineers, and IT teams to ensure scalable and maintainable implementations
• Measure and communicate business impact of AI solutions using clear metrics and outcomes
• Document system designs, models, and operational processes to support knowledge sharing and scalability
• Manages, leads, and/or assists various new or sustaining technology projects; performs light project management duties as required
• Stay current with applied AI trends and recommend practical innovations that align with business goals
Qualifications:
Required:
• Bachelor’s degree in computer science, engineering, data science, mathematics, or a related technical field, or equivalent practical experience
• Successful candidates typically possess over 8 years of relevant professional or technical engineering experience of increasing responsibility and difficulty of assignments
• Experience building and deploying applied AI or machine learning solutions in production environments
• Hands on experience with at least one machine learning framework such as scikit learn, PyTorch, or TensorFlow
• Practical experience using large language models via APIs for real world business use cases
• Experience designing and implementing AI driven automation or agentic workflows
• Programming languages: Python for building AI models, automation, and production services
• SQL for working with structured data used in analytics, forecasting, and model inputs
• Strong understanding of data pipelines, feature engineering, and data quality fundamentals
• Experience integrating AI solutions with existing enterprise systems using APIs
• Familiarity with cloud-based AI services and deployment patterns
• Applied model evaluation and testing expertise, including prompt testing, experimentation and A/B testing, system integration testing and production monitoring
• Experience optimizing costs for LLMs and agentic systems in cloud environments, including inference efficiency, token usage, and model selection tradeoffs
• Understanding of software engineering best practices including version control, testing, and documentation
• Knowledge of security, privacy, and responsible AI considerations in business environments
Preferred:
• JavaScript or TypeScript for integrating AI capabilities into web applications or internal tools
• Bash or shell scripting for automation and deployment tasks
• Experience with agentic AI frameworks or orchestration tools such as LangChain, LlamaIndex, AutoGen, or MCP based patterns
• Experience with cloud platforms such as Azure, AWS or GCP beyond basic usage
• Familiarity or experience with Dynamics 365, Snowflake and Microsoft Fabric
• Familiarity with MLOps practices including model monitoring, versioning, and lifecycle management
• Experience with data pipeline and workflow tools such as Airflow, dbt, or cloud native orchestration services
• Applied statistics and model evaluation skills, including experiment design and statistical validation, to ensure reliable and unbiased AI driven decision support and forecasting
• Experience evaluating and integrating third party AI platforms or vendors
• Exposure to automation platforms, RPA tools, or workflow engines where AI is embedded into business processes
• Familiarity with emerging AI regulations and auditability techniques
• Ability to mentor or guide others on applied AI best practices
Company:
Innovation. Creativity. Evolution. If these are words you’re passionate about, we should chat. Founded in 1964, the company is headquartered in Newberg, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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