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Privacy Preserving Machine Learning Jobs in Indianapolis, IN

NEOGOV

Solution Architect

Indianapolis, IN · On-site

$55.25 - $72.75/hr

... Ensure data privacy, tenant isolation, and governance in multi-tenant systems • Support ... machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) • Experience with building and ...

Cisco

Applied AI Scientist

Indianapolis, IN · On-site

Contribute to the research, design, and development of large-scale foundation models for machine ... preserving accuracy. * MLOps & Continuous Learning - Fluency in automated retraining, drift ...

Crowe LLP

Cyber AI Security Manager

Indianapolis, IN · On-site +1

$106K - $143K/yr

Experience with data anonymization techniques and privacy-preserving machine learning methodologies. * Understanding of DevSecOps principles and experience integrating security into CI/CD pipelines.

Deloitte

Lead Forward Deployed Engineer - AWS

Indianapolis, IN · On-site

$98K - $129K/yr

... Machine Learning Engineer (Associate - MLA-C01) or AWS Certified Data Engineer (Associate) * 1+ ... Familiarity with security, privacy, and compliance considerations The wage range for this role ...

Lifepoint Health

Pharmacy Technician. (PRN)

Indianapolis, IN · On-site

$16.75 - $20.25/hr

... to medication machines * Restocks usable medications/IVs returned to the pharmacy * Files ... Ongoing learning and career advancement opportunities. Qualifications and requirements: * High ...

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All JobsPrivacy Preserving Machine Learning JobsPrivacy Preserving Machine Learning Jobs in Indianapolis, IN

Privacy Preserving Machine Learning information

See Indianapolis, IN salary details

$95.1K

$110.4K

$123.8K

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 Indianapolis, IN is $110,407.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,500.00 and $123,300.00 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 Indianapolis, IN?

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

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

The top searched job categories for Privacy Preserving Machine Learning jobs in Indianapolis, IN are:

What cities near Indianapolis, IN are hiring for Privacy Preserving Machine Learning jobs?

Cities near Indianapolis, IN 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 Indianapolis, IN as of August 2026, with employment types broken down into 100% Full Time. Highlights an 73% In-person, and 27% Remote job distribution, with an average salary of $110,407 per year, or $53.1 per hour.

Postdoctoral Fellow in Biostatistics & Health Data Science

Indiana University School of Medicine

Indianapolis, IN • On-site

$46K - $63K/yr

Full-time

Re-posted 5 hours ago

Job description

Postdoctoral Fellow in Biostatistics & Health Data Science

Indiana University is an equal opportunity employer and provider of ADA services and prohibits discrimination in hiring. See Indiana University's Notice of Non-Discrimination here which includes contact information.

The Annual Security and Fire Safety Report, containing policy statements, crime and fire statistics for all Indiana University campuses, is available online. You may also request a physical copy by emailing IU Public Safety at iups@iu.edu

The postdoctoral position addresses a fundamental and timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization?

We are particularly interested in:

  • LLM-driven systems for aligning real-world health data to standards like OMOPCDM, FHIR, and UMLS
  • Agent-based workflows that explain, refine, and adapt semantic mappings over time
  • Hybrid architectures that combine knowledge-grounded reasoning with flexible machine learning
  • Tools that reduce manual burden while preserving traceability and clinical interpretability

This position offers the opportunity to publish novel methods, work with real messy multi-source data, and contribute to infrastructure supporting population-level research and health equity.

The postdoctoral fellow will be based in the Department of Biostatistics and Health Data Science at Indiana University School of Medicine, in close collaboration with the Regenstrief Institute, a nationally renowned center for health informatics research and real-world data infrastructure.

Our Team's Approach-We are not a pure research group. We operate at the interface of research and health data operations, building methods that not only publish but also deploy. We handle real clinical and public health data problems where ambiguity, variation, and scale are the norm—not the exception.

We welcome postdocs who want to drive innovation while engaging deeply with practical, meaningful data challenges.

Responsibilities:

  • Design and implement LLM-based methods for clinical data harmonization, semantic normalization, and ontology alignment
  • Develop multi-agent or RAG-style (retrieval-augmented generation) workflows for schema matching and terminology mapping
  • Collaborate with national and multi-institutional initiatives in data integration and standardization
  • Support open-source tooling, reproducible pipelines, and standards-based approaches (e.g., OMOP, FHIR, UMLS)
  • Lead or support manuscript preparation and dissemination at top informatics and AI venues
  • Contribute to grant development and proposal writing

What We Offer:

  • A collaborative environment at the intersection of real-world data, applied AI, and translational science
  • Opportunities to work across academic, clinical, and public health settings
  • Mentorship and support toward independent research or career development in academia or industry
  • Competitive salary and benefits through Indiana University
  • A culture that values both scientific innovation and practical impact

The Indianapolis Campus is the focal point of health professions education at Indiana University, and the School of Medicine is the country's second largest allopathic medical school. Indianapolis consistently ranks high nationally on many of the "best places to live" lists and has an economy that is growing in the life sciences arena. In addition, it has always been one of the cities with the lowest cost of living. Carmel, Indy's northern neighbor, was recently named as the best mid-sized city in the country.

IUSM is committed to being a welcoming campus community and we seek candidates whose research, teaching, and community engagement efforts contribute to robust learning and working environments for all students, staff, and faculty. We invite individuals who will join us in our mission to improve health equity and well-being for all throughout the state of Indiana.

Indianapolis is the capital and most populous city in the State of Indiana. It is growing economically thanks to a strong corporate base anchored by the life sciences. Indiana is home to one of the largest concentrations of health sciences companies in the nation. Indianapolis has a sophisticated blend of charm and culture with a wonderful balance of business and leisure. The growing residential base is supported by rich amenities and quality of life – the city possesses a variety of professional sports, arts venues and outdoor recreation areas. Residents of this dynamic city, and surrounding suburbs, enjoy leading educational systems and top-ranked universities, paired with a diverse population. Indianapolis International Airport is a top-ranked international airport, being named "Best Airport in North America" by Airports Council International for many years.

For additional information on life in Indy: https://faculty.medicine.iu.edu/relocation.

The search will continue until the positions are filled.

Basic Qualifications - Required Qualifications:

  • Ph.D. (by start date) in Computer Science, Biomedical Informatics, Health Data Science, Biostatistics, or a closely related area.
  • Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP.
  • Demonstrated working experience with healthcare data (e.g., EHR, clinical text, imaging, omics).
  • Proficiency in Python and ML tooling (e.g., PyTorch, scikit-learn), version control (Git), and experiment tracking (e.g., Weights & Biases).
  • Excellent written and oral communication skills, and ability to collaborate with multidisciplinary teams.

Department Contact for Questions - Professor Jiang Bian via email at: bianj@regenstrief.org

Additional Qualifications - Preferred Qualifications:

  • Experience with concept normalization, ontology mapping, or schema alignment
  • Familiarity with LLM agents, tool-augmented reasoning, or hybrid rules + LLM systems
  • Record of publications in relevant domains (informatics, machine learning, AI, knowledge representation)
  • Experience with multi-site data harmonization or federated data environments

Special Instructions

Priority Application Review Deadline

Expected Start Date

Posting Number - IUSM-02358-2026

Supplemental Questions

Required fields are indicated with an asterisk (*).

  • * How did you hear about this position?
    • Personal Contact: At Professional Meeting or Conference
    • Personal Contact: Direct Contact by Search Committee
    • Personal Contact: Referred by colleague or advisor
    • Personal Contact: School of Medicine recruiter
    • Personal Contact: IUHP Physician Recruiter
    • Announcement: Other Journal or Magazine
    • Announcement: Other Website
  • * Are you a dual career partner (your partner or spouse is already being recruited)?
    • Yes
    • No

Applicant Documents

Required Documents

  • Curriculum Vitae
  • Letter of Application
  • List Of References

Optional Documents

Apply