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Privacy Preserving Machine Learning Jobs in Palatine, IL

Career Renew

Senior Machine Learning Engineer

Chicago, IL · Remote

$165K - $225K/yr

Our solutions deliver diagnostic-quality results in minutes while preserving tissue samples for ... The ideal candidate will have deep expertise in Machine Learning and building generalizable ...

Cccis

Senior Manager - Machine Learning

Chicago, IL · On-site

The Role We are seeking a Senior Manager to lead our Machine Learning (ML) team in the subrogation ... Our Job Applicant Privacy Notice is available HERE. About CCC's Commitment to Employees: CCC ...

Orion

Senior Privacy & AI Risk Analyst

Mundelein, IL · Hybrid

Perform privacy reviews and impact assessments for AI and machine-learning use cases that involve personal information, evaluating data minimization, purpose limitation, transparency, and automated ...

DRW

AI Engineer

Chicago, IL · On-site

$175K - $300K/yr

You'll work on high-impact machine learning (ML) and artificial intelligence (AI) initiatives that ... Privacy Notice for information about certain legal rights at #LI-BL1

Accenture

Senior Data Scientist

Chicago, IL · On-site

Develop supervised and unsupervised machine learning solutions, including classification ... Experience with AI governance, Responsible AI, model risk, data ethics, privacy, security ...

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All JobsPrivacy Preserving Machine Learning JobsPrivacy Preserving Machine Learning Jobs in Palatine, IL

Privacy Preserving Machine Learning information

See Palatine, IL salary details

$100K

$116.1K

$130.2K

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

As of Aug 12, 2026, the average yearly pay for privacy preserving machine learning in Palatine, IL is $116,092.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $129,700.00 per year, depending on experience, location, and employer.

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 cities near Palatine, IL are hiring for Privacy Preserving Machine Learning jobs? Cities near Palatine, IL 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 Palatine, IL as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $116,092 per year, or $55.8 per hour.

Machine Learning Engineer / Scientist

Until

Mundelein, IL

$140K - $240K/yr

Full-time

Re-posted 19 days ago

Job description

Until is a moonshot company building a “pause button” for biology. Our near-term focus is organ-scale reversible cryopreservation: preserving donated organs at subzero temperatures without ice formation, then rewarming them uniformly for transplant. By solving this grand challenge, we’re laying the foundation for whole-body reversible cryopreservation, giving patients a bridge to future cures.

To achieve our goal, we are assembling an interdisciplinary team to develop perfusion systems, cryoprotectant formulations, and vitrification and rewarming hardware. We are also building out our medical hibernation team to tackle the challenges of whole-body cryopreservation, beginning with rodent models.

We envision a future where no transplantable organ is lost to logistics, and no terminal diagnosis is final because patients can safely wait for future medicine to arrive.

About the Role
As a Machine Learning Engineer / Scientist at Until, you will be an early member of the computational team defining how experimental data becomes insight and drives the next round of scientific discovery. You’ll build high-leverage ML systems that help develop new cryoprotectant formulations, engineer biologically-inspired antifreeze proteins, and understand the physics of vitrification and rewarming. You will own projects end-to-end including shaping data collection and designing data pipelines, training and evaluating models, and deploying tooling that scientists use daily. 
About You
  • Degree in Computer Science or a related field (Applied Mathematics, Statistics, Data Science, Computational Biology).
  • Excellent foundations in the mathematics that underlies machine learning, including linear algebra, probability, statistics, and calculus. 
  • Strong experience in modern machine learning approaches, such as representation learning, generative modeling, active learning, and bayesian optimization.
  • Track record of developing ML approaches for scientific discovery, as evidenced by a strong publication record, substantial open source contributions, or deployment of a machine learning system in an industry role.
  • Demonstrated ability to write modular, maintainable, and performant code in Python.
  • Fluency with the Python data science and ML stack, including PyTorch, NumPy, SciPy, Pandas/Polars, Matplotlib/Plotly.
  • Proficient with developer tooling, including Linux command line, Git, and shell scripting.
  • Ability to think from first principles and tackle complex, cross-disciplinary problems with other scientists and engineers.
Preferred Qualifications
  • 3+ years of relevant professional or research experience, or a PhD in a computational field.
  • Strong understanding of computer science fundamentals, including algorithms, operating systems, and concurrency. 
  • Experience with cloud infrastructure (AWS, GCP) and SQL databases.
Benefits
  • Opportunity for outsized impact creating the future as an early team member
  • Generous medical, dental and vision insurance coverage
  • Flexible time off and paid holidays
  • Competitive compensation package, including salary and equity
  • 401(k) retirement savings plan
  • FSA and commuter benefits
  • Subsidized lunch daily
While this represents our expected range based on market data, final compensation will be determined based on your specific qualifications and may be outside this range. Please keep in mind that the equity portion of the offer is not included in this estimate.
As an equal opportunity employer, Until is committed to providing employment opportunities to all individuals. All applicants for positions at Until will be treated without regard to race, color, ethnicity, religion, sex, gender, gender identity and expression, sexual orientation, national origin, disability, age, marital status, veteran status, pregnancy, or any other basis prohibited by applicable law.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.