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Remote Nvidia Engineering Jobs in Chicago, IL (NOW HIRING)

Career Renew

Senior Machine Learning Engineer

Chicago, IL · Remote

$165K - $225K/yr

... remote role for US/Canada based candidates. Salary range: 165-225K USD yearly plus benefits plus ... Knowledge of software engineering best practices including version control (Git) and CI/CD ...

Sonar

Major Territory Manager (Remote)

Chicago, IL · Remote

... assisted developers or autonomous agents is reliable, secure, and maintainable. Integrating ... Industry giants like Nvidia, ServiceNow, Booking.com, Goldman Sachs, AstraZeneca, and Ford Motor ...

Humana

Lead, Network Architect - DC Core

Chicago, IL · On-site +1

The Lead, Network Architect Provides Level 3 design, engineering implementation & support for the ... Enterprise hands-on experience w/AI/NVIDIA designs, deployment and operations * Enterprise hands-on ...

Humana

Lead, Network Architect - DC Core

Chicago, IL · On-site +1

The Lead, Network Architect Provides Level 3 design, engineering implementation & support for the ... Enterprise hands-on experience w/AI/NVIDIA designs, deployment and operations * Enterprise hands-on ...

Humana

Lead, Network Architect - DC Core

Chicago, IL · On-site +1

The Lead, Network Architect Provides Level 3 design, engineering implementation & support for the ... Enterprise hands-on experience w/AI/NVIDIA designs, deployment and operations * Enterprise hands-on ...

All JobsNvidia Engineering JobsRemote Nvidia Engineering JobsRemote Nvidia Engineering Jobs in Chicago, IL

Remote Nvidia Engineering information

See Chicago, IL salary details

$58.7K

$141.1K

$202.9K

How much do remote nvidia engineering jobs pay per year?

As of Jul 28, 2026, the average yearly pay for remote nvidia engineering in Chicago, IL is $141,136.00, according to ZipRecruiter salary data. Most workers in this role earn between $125,200.00 and $156,100.00 per year, depending on experience, location, and employer.

What is a Remote Nvidia Engineer?

A Remote Nvidia Engineer is a professional who works for Nvidia, or with Nvidia technologies, from a location outside of a traditional office setting. These engineers may specialize in areas such as GPU development, AI research, software engineering, or hardware design, and they collaborate with teams virtually. Remote Nvidia Engineers use digital tools to communicate, manage projects, and contribute to cutting-edge technologies in graphics processing, artificial intelligence, and computing platforms. The remote aspect allows for flexible work arrangements and the ability to participate in global projects.

What are some common challenges faced by engineers working remotely for Nvidia, and how can they be overcome?

Remote engineers at Nvidia often encounter challenges related to communication across time zones, staying aligned with fast-paced project developments, and maintaining visibility within distributed teams. To overcome these, it's important to proactively engage in virtual meetings, leverage collaboration tools like Slack and Jira, and regularly update your team on progress. Building strong relationships with peers and seeking out mentorship opportunities can also help remote engineers stay connected and advance within the company.

What are the key skills and qualifications needed to thrive as a Remote Nvidia Engineer, and why are they important?

To excel as a Remote Nvidia Engineer, you typically need a strong background in computer engineering, programming (e.g., C++, Python), and experience with GPU architectures, often supported by a relevant degree. Familiarity with Nvidia tools like CUDA, cuDNN, and deep learning frameworks, as well as proficiency in remote collaboration platforms, are crucial. Strong problem-solving skills, self-motivation, and effective communication are vital soft skills for working independently and collaborating across distributed teams. These competencies ensure efficient development, troubleshooting, and innovation in Nvidia's complex, high-performance computing environments.

What is the difference between Remote Nvidia Engineering vs Remote Nvidia Data Scientist?

AspectRemote Nvidia EngineeringRemote Nvidia Data Scientist
Required CredentialsBachelor's in Engineering, Computer Science, or related field; experience with GPU programmingBachelor's or higher in Data Science, Statistics, or related; proficiency in machine learning and data analysis
Work EnvironmentDesign, develop, and optimize GPU hardware/software; collaborative teamsAnalyze large datasets, develop models, and generate insights; often cross-functional teams
Employer & Industry UsagePrimarily in hardware, AI, and high-performance computing sectorsPrimarily in AI, analytics, and research sectors

Remote Nvidia Engineering focuses on hardware and software development for GPUs, requiring engineering credentials and technical skills. Remote Nvidia Data Scientists analyze data and build models, requiring expertise in data science. Both roles are remote, but they serve different functions within Nvidia's ecosystem.

What are the most commonly searched types of Nvidia Engineering jobs in Chicago, IL? The most popular types of Nvidia Engineering jobs in Chicago, IL are:
What are popular job titles related to Remote Nvidia Engineering jobs in Chicago, IL? For Remote Nvidia Engineering jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Remote Nvidia Engineering jobs in Chicago, IL look for? The top searched job categories for Remote Nvidia Engineering jobs in Chicago, IL are:
Remote Nvidia Engineering jobs near you
Digital Signal Processing (DSP) Engineer - AI/ML Ops / Remote

Digital Signal Processing (DSP) Engineer - AI/ML Ops / Remote

Apetan Consulting llc

Chicago, IL • Remote

$80 - $150/hr

Contractor

Posted 14 days ago

Job description

Digital Signal Processing (DSP) Engineer – AI/ML Ops
Location: Chicago, IL (Hybrid/Onsite Preferred)
 
Responsibilities
• Design and implement advanced DSP algorithms for real-time and offline signal processing.
• Develop AI/ML models for signal classification, anomaly detection, feature extraction, and predictive analytics.
• Build scalable data pipelines for signal acquisition, preprocessing, and model training.
• Deploy ML models into production using MLOps best practices.
• Optimize DSP and AI algorithms for latency, throughput, and computational efficiency.
• Collaborate with data scientists, embedded engineers, and software development teams.
• Implement CI/CD pipelines for machine learning workflows.
• Monitor production models for drift, performance, and reliability.
• Work with cloud-native AI services and containerized deployments.
• Document architecture, algorithms, and deployment processes.
 
Required Qualifications
• Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
• 5+ years of experience in Digital Signal Processing.
• Strong knowledge of: o Digital Filters o FFT o Wavelets o Spectral Analysis o Adaptive Filtering o Time-Series Signal Processing
• Proficiency in Python and C/C++.
• Experience with TensorFlow or PyTorch.
• Hands-on experience building ML pipelines.
• Experience with Docker and Kubernetes.
• Experience with Git and CI/CD. Preferred Qualifications
• Experience with MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
• Experience deploying AI models at the edge.
• Familiarity with NVIDIA CUDA or GPU optimization.
• Experience with audio, radar, RF, image, LiDAR, or sensor signal processing.
• Knowledge of LLMs and Agentic AI is a plus.
• Experience working in regulated industries (Healthcare, Automotive, Aerospace, Telecom, Industrial).